Patent Publication Number: US-6993535-B2

Title: Business method and apparatus for employing induced multimedia classifiers based on unified representation of features reflecting disparate modalities

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   The present application is related to U.S. patent application Ser. No. 09/853,191, entitled “Method and Apparatus for Inducing Classifiers for Multimedia Based on Unified Representation of Features Reflecting Disparate Modalities,” by inventors R. M. Bolle, N. Haas, F. J. Oles, and T. Zhang, filed on May 10, 2001. 
   FIELD OF THE INVENTION 
   This invention relates to the business of handling multimedia information (media items), such as video and images that have audio associated with it or possibly have text associated with it in the form of captions. More specifically, the invention relates to the business of handling video and audio by processing the video and audio for supervised and unsupervised machine learning of categorization techniques based on disparate information sources such as visual information and speech transcript. The invention also relates to combining these disparate information sources in a coherent fashion to make business decisions. 
   BACKGROUND OF THE INVENTION 
   Beyond data that can be represented in machine readable tabular form and, of course, machine readable text documents, many other forms of media are transitioning to machine readable digital form. For example, visual data such as images and video are increasingly being produced in digital form or converted to digital form. Large collections and catalogues of these media objects need to be organized, similarly to structured text data, but using categorization technology enhanced with new technologies that allow for convenient categorization based on visual and audio content of the media. Such collections of media are managed using multimedia databases where the data that are stored are combinations of numerical, textual, auditory and visual data. 
   Video is a special, peculiar type of data object in the sense that there is a notion of time associated with this data. These types of data are referred to as streamed information, streamed multimedia data or temporal media. When transporting this data from one location to some other location for viewing purposes, it is important that the data arrives in the right order and at the right time. In other words, if frame n of a video is displayed at time t, frame n+1 has to be at the viewing location at time t plus 1/30th of a second. Of course, if the media are moved or transported for other purposes, there is no such requirement. 
   Similarly to text documents, which can be segmented into sections, paragraphs and sentences, temporal media data can be divided up into smaller more or less meaningful time-continuous chunks. For video data, these chunks are often referred to as scenes, segments and shots, where a shot is the continuous depiction of space by a single camera between the time the camera is switched on and switched off, i.e., it is an image of continuous space-time. In this disclosure, we refer to these temporal, time-continuous (but not necessarily space-continuous) chunks of media as media items. These media items include image and video, with associated audio or text and, in general, information stream items composed of disparate sources of information. Examples of media items are commercial segments (or groups) broadcast at regular time intervals on almost every TV channel; a single commercial is another example of a media item or video segment. 
   Multimedia databases may contain collections of such temporal media items in addition to non-streamed media objects such as still images and text documents. Associated with the media items may be global textual or parametric data, such as the name of the director of the video/music (audio) or the date of recording. Categorization of these media items into classes can be accomplished through supervised and unsupervised clustering and decision tree generation based on the text and, possibly, parametric data. 
   Multimedia collections may also be categorized based on data content, such as the amount of green or red in images or video and sound frequency components of audio segments. The media item collections have to be then preprocessed and the results have to be somehow categorized based on the visual properties. Categorizing media items based on semantic content, the actual meaning (subjects and objects) of the media items, on the other hand, is a difficult issue. For video, speech may be categorized or recognized to some extent, but beyond that, the situation is much more complicated because of the rudimentary state of the art in machine-interpretation of visual data. 
   Determining whether a given media item is equal to (a piece of) one of, or is similar to (a piece of) one of, a plurality of temporal media items; or, determining whether it is equal or similar to a media item or equal or similar to a sub segment in a media item collection is another important multimedia categorization problem. A variant here is the issue of determining if a given temporal input media item contains a segment which is equal or similar to one of a plurality of temporal media stream segments or determining if the input stream contains a segment which is equal or similar to a media item in a multimedia database. To achieve this one needs to somehow compare a temporal media item to a plurality of temporal media items or databases of such items. This problem arises when certain media items need to be selected or deselected in a given temporal media item or in a plurality of temporal media items. An example here is the problem of deselecting or suppressing repetitive media items in a television broadcast program. Such repetitive media items can be commercials or commercial segments or groups which are suppressed either by muting the sound channel or by both muting the sound channel and blanking the visual channel. 
   To develop a procedure for identifying media items as belonging to particular classes or categories, (or for any classification or pattern recognition task, for that matter) supervised learning technology can be based on decision trees, on logical rules, or on other mathematical techniques such as linear discriminant methods (including perceptrons, support vector machines, and related variants), nearest neighbor methods, Bayesian inference, etc. We can generically refer to the output of such supervised learning systems as classifiers. 
   Supervised learning technology requires a training set consisting of labeled data, that is, representations of previously categorized media segments, to enable a computer to induce patterns that allow it to categorize hitherto unseen media segments. Generally, there is also a test set, also consisting of labeled data, that is used to evaluate whatever specific categorization procedure is developed. In academic exercises, the test set is usually disjoint from the training set to compensate for the phenomenon of overfitting. In practice, it may be difficult to get large amounts of labeled data of high quality. If the labeled data set is small, the only way to get any useful results at all may be to use all the available data in both the training set and the test set. 
   To apply standard approaches to supervised learning, the media segments in both the training set and the test set must be represented in terms of numbers derived from counting occurrences of features. The relationship between features extracted for the purposes of supervised learning and the content of a media segment has an important impact on the success of the enterprise, so it has to be addressed, but it is not part of supervised learning per se. 
   From these feature vectors, the computer induces classifiers based on patterns or properties that characterize when a media segment belongs to a particular category. The term “pattern” is meant to be very general. These patterns or properties may be presented as rules, which may sometimes be easily understood by a human being, or in other, less accessible formats, such as a weight vector and threshold used to partition a vector space with a hyperplane. Exactly what constitutes a pattern or property in a classifier depends on the particular machine learning technology employed. To use a classifier to categorize incoming hitherto unseen media segments, the newly arriving data must not only be put into a format corresponding to the original format of the training data, but it must then undergo a further transformation based on the list of features extracted from the training data in the training phase, so that it finally possesses a representation as a feature vector that permits the presence or absence of the relevant patterns or properties to be determined. 
   The assignment of more than one category to an item is called multiple categorization. Some supervised learning techniques (for example, a few, but not all, approaches using decision trees) do not support multiple categorization. They make the assumption that each item categorized will belong to at most one category, which may not be adequate in some applications. Some supervised learning systems may return a ranked list of possibilities instead of a single category, but this is still slightly deficient for some applications, because such a system might assign categories even to items that should be placed in no category. What are usually most useful are those supervised learning methods that give realistic confidence levels with each assigned category. 
   The idea behind text feature selection is that the occurrence of the selected features in text associated with an unclassified data item will be a useful ingredient for the development of an automated classification system designed to assign one or more categories to the data item. For text data, the first processing step that must be done is tokenization, i.e., the segmentation of a string of characters into a string of words or tokens. However, the representation of an item of text data as a string of arbitrary words, with all of the meaningful linguistic structures it implicitly contains, is often simply too complicated and rich for a computer to handle. Even if one does no parsing of the text, there may well be too many potential features, in which case some distillation is needed. Luckily, single words themselves have been seen to comprise an adequate set of features for many supervised learning problems. Sometimes it is useful to identify the part of speech of each word, thus distinguishing between an instance of the verb walk and the noun walk. (This is called part-of-speech tagging.) This only scratches the surface. Modern techniques of computational linguistics permit the identification of complex features in text, but with rising complexity comes vast numbers of features. At any rate, after the training set is prepared, and after the text associated with it is identified, a list of those text features deemed particularly relevant to the particular classification task at hand is automatically extracted. Call the features in this list the extracted text features, and call the process of building the list text feature extraction. There is an issue in regard to whether a single list of features, called in this setting a global dictionary, is created or whether there is a separate list for each category, called in this context local dictionaries. The resolution of this issue can depend on the details of the supervised learning technique employed, but, in applications related to text, local dictionaries generally give better performance. There are a variety of criteria for judging relevance during feature extraction. A simple one is to use absolute or normalized frequency to compile a list of a fixed number n of the most frequent features for each category, taking into account the fact that small categories may be so underpopulated that the total number of features in them may be less than n. More sophisticated techniques for judging relevance involve the use of information-theoretic measures such as entropy or the use of statistical methods such as principal component analysis. 
   After text feature extraction, a new vector representation of each text item associated with the training data is then extracted in terms of how frequently each selected feature occurs in that item. The vector representation may be binary, simply indicating the presence or absence of each feature, or it may be numeric in which each numeric value is derived from a count of the number of occurrences of each feature. 
   A large body of prior art of video processing for video identification, detection, categorization, and classification is concerned with the detection of commercials in a video stream, i.e., the media item is a commercial or a sequence of commercials. This is not a categorization problem per se, but rather a detection problem. The detection of one class (or category) of interest, though, is in itself a categorization problem, where the categories are “category-of-interest” and “unknown.” 
   Many methods rely on the fact that commercials are often surrounded by blank frames, changes in audio/brightness level, simple representations of intermediate frames and more global dynamic properties that typically hold for commercials. An example of a method and apparatus for detection and identification of portions of temporal video streams containing commercials is described in U.S. Pat. No. 5,151,788 to Blum. Here, a blank frame is detected in the video stream and the video stream is tested for “activity” (properties such as sound level, brightness level and average shot length). U.S. Pat. No. 5,696,866 to Iggulden et al. extend the idea to detecting a “flat” frame. In addition to a frame being flat at the beginning and end of a commercial, they include that the frame has to be silent. Additional features, such as changes in the audio power or amplitude and changes in brightness of the luminance signal between program and commercial segments, of the video signal are used in U.S. Pat. No. 5,343,251 to Nafeh. 
   Many techniques for detecting commercials, reduce commercials to a small set of representative frames, or key frames, and then use image matching schemes to match the key frames. Here, each particular commercial has some representation, instead of using generic attributes above that describe the category of commercials. For example, U.S. Pat. No. 5,708,477 to S. J. Forbes et al. uses the notion of a list of abbreviated frames for representing commercial video segments. An abbreviated frame is an array of digital values representing the average intensities of the pixels in a particular portion of the video frame. Upon detection of a scene change in the live video stream, computed and stored abbreviated frames are matched and commercial is detected and classified (if present in memory). A technique that uses more sophisticated frame representations is presented in reference:
         J. M. Sanchez, X. Binefa, J. Vitria, and P. Radeva, Local color analysis for scene break detection applied to TV commercial recognition, Third International Conference, Visual&#39;99, Amsterdam, June 1999, pp. 237–244.       

   (This reference is incorporated herein in its entirety.) Each commercial in the database is represented by a number of color histograms, or color frequency vectors, for a representative frame for each shot in the commercial. The shot boundaries of a commercial are detected by some shot boundary detection algorithm (finding scene breaks). Commercials are detected in a live video stream by comparing all the color histograms of all the commercials to the color histograms representing a shot in video stream. No temporal information is incorporated in the representation of the commercial. 
   All this prior art falls in the realm of detection of video copies. The use of image feature histograms, where the images are particular video frames, like shot boundaries, have been popularized in the area of image recognition, and, later on in the area of image search. Color histograms (color frequency distributions) are the most widely used, in particular, the Red Green Blue (RGB) and the Hue Saturation and Intensity (HSI). Other color spaces that could be used are those defined by the CIE ( Commission Internationale de l&#39;Eclairage —the International Committee for Illumination). These spaces are CIE L*u*v* hue angle and saturation and CIE L*a*v* hue angle and saturation. Ratios of color components such as the red response divided by the green response (after appropriate gamma correction) also yield intensity independent color measures. Another popular method is to divide each response by the average response across all spectral bands, such as Rn=R/(R+G+B), to produce a set of fractional color components (which sum to one). 
   A particular instance of image database search, is image classification, or image content recognition. In an image classification problem, typically, the number of classes is smaller than the number of images in an image database. An example of image classification is found in:
         R. Bolle, J. Connell, G. Taubin, N. Haas, R. Mohan, “VeggieVision: A produce recognition system,” in Proc. Third IEEE Workshop on Applications of Computer Vision, pp. 244–251, December 1996.       

   This reference is incorporated herein in its entirety. Color histograms are used in this work, but the use of color frequency distributions is extended to the use of histograms to represent other features that are used for image/object classification. Histograms are a compact representation of a reference image that do not depend on the location or orientation of the object in the image, or, at least, depend only a little because of quantization effects. For example, visual texture is a feature used in “VeggieVision” to Bolle et al. As opposed to color, texture is a visual feature that is much more difficult to describe and to capture computationally. It is also a feature that cannot be attributed to a single pixel but rather is attributed to a patch of image data. The texture of an image patch is a description of the spatial brightness variation in that patch. This can be a repetitive pattern of primitives (texels), or, can be more random, i.e., structural textures and statistical textures. Computational texture measures are either region-based or edge-based, trying to capture structural textures and statistical textures, respectively. In “VeggieVision” to Bolle et al., a texture representation of an image, image class, or image category, then, is a one-dimensional histogram of local texture feature values. Shape can also be represented in terms of frequency distribution. The information available to work with is the two-dimensional boundary of (say) a segmented image. Boundary shape is a feature of multiple boundary pixels and is expressed by a local computational feature, for example, curvature. Local curvature is estimated by fitting a circle at each point of the boundary. After smoothing, this boundary shape feature is quantized and a histogram is computed. Instead of over an area, such as for color histograms, these histograms are computed from a collection of image pixels that form the boundary of the object image. Finally, size of image segments is another feature of the images that is important in “VeggieVision” to Bolle et al. A method that computes area from many collections of three boundary points is proposed. Three points determine a circle and, hence, a diameter D. A histogram of these diameter estimates is then used as a representation for objects (in the image) size. 
   Many video copy detection solutions use some spatial representation of frames or images (spatial representations as described above) and some temporal representation of the times between the frames, i.e., a spatial-temporal representation. Indyk et al. have proposed a method for video copy detection, solely using the distance (time) between shot breaks in the video as the feature of the video.
         P. Indyk, G. Iyengar and N. Shivakumar, Finding pirated video sequences on the Internet. tech. rep., Stanford Infolab, February 1999.       

   This method (incorporated herein by reference) is somewhat limited in the richness of the representation. Other video copy detection algorithms use some form of image matching (visual data) combined with temporal evidence integration. A method for detecting arbitrary video sequences, including commercials, is described in (incorporated herein by reference):
         R. Mohan, “Video Sequence Matching”, International Conference on Acoustics, Speech and Signal Processing, (ICASSP), May 1998.       

   Mohan defines that there is a match between a given video sequence and some segment of a database video sequence if each frame in the given sequence matches the corresponding frame in the database video segment. That is, the matching sequences are of the same temporal length; matching slow-motion sequences is performed by temporal sub-sampling of the database segments. The representation of a video segment is a vector of representations of the constituent frames in the form of an ordinal measure of a reduced intensity image of each frame. Before matching, the database is prepared for video sequence by computing the ordinal measure for each frame in each video segment in the database. Finding a match between some given action video sequence and the databases then amounts to sequentially matching the input sequence against each sub-sequence in the database and detecting minimums. This method introduces the temporal aspects of the video media items. 
   All these color-based image methods are subject to digitizing and encoding artifacts, like color variations. See A. Hampapur and R. M. Bolle, Feature based Indexing for Media Tracking. In Proc. of Int. Conf. on Multimedia and Expo, August 2000, pp. 67–70 (Hampapur et al.). To circumvent color variations Hampapur et al. have, instead, used other features that are invariant to color variations. In a first, off-line indexing phase representations for a set of known reference media items are computed and stored in an index structure. For each segment, a set of intervals is determined and from each key interval, a set of feature values is extracted from portions of the video frames. The values are quantized and index tables are built where feature values point to the reference media items. In the search and detection phase, a real-time process of computing and quantizing features from a target media stream is done in the same fashion. Additionally, counters are initialized for each of the known media items. When computed feature values point to a known media item, the corresponding counter is incremented. High values of the counter indicate the presence of a known media item in the target stream. An interesting thing to note here is that any feature type, such as, color, edges or motion, can be used in this method. Further, features are not computed on a frame basis (as in the above methods) but rather from regions within the frame and even regions of consecutive frames (local optical) flow. Detecting media items is further accomplished with a computational complexity that is sub-linear. 
   Reference Hampapur et al. is incorporated herein by reference. 
   What all these above mentioned references have in common is that the visual features extracted from the video do not have a whole lot of semantic meaning, e.g., a color, in and of itself, does not say much about the semantic content of the image or video. See Lienhart, C. Kuhmunch and W. Effelsberg, “On the detection and recognition of television commercials.” In Proc. of the IEEE Conf. on Multimedia Computing and Systems, 1997 (Lienhart et al.). Lienhart et al. take things a step further. They describe a system for performing both feature based detection and recognition of known commercials. The visual features that are used have spatial-temporal aspects. They use directly measurable features, such as, a spot being no longer than 30 seconds, spots being separated by a short break of 5–12 monochrome frames, and the volume of the audio signal being turned up. In addition, they use indirectly measurable features, like the fact that spots are full of motion, animated, and full of action. In addition, commercial spots have many still frames and many of them contain textual information. It is important to note that these are the beginnings of image processing techniques for extracting semantic information, such as action and motion, from video frames. 
   Reference Lienhart et al. is incorporated herein by reference. 
   Now consider B-T Truong, S. Venkatesh and C. Dorai, “Automatic Genre Identification for Content-Based Categorization,” in Proc. Int. Conf. On Pattern Recognition, September 2000, pp. 230–233 (B-T Truong et al.), incorporated herein in its entirety. The authors take the use of extracted semantic features a step further. The extracted visual features have cinematographic meaning, such as, fades, dissolves and motion features. Motion features are incorporated in terms of “quiet” visual scenes (the absence of motion) and “motion runs,” unbroken sequences of motion, where motion is defined in terms of luminance differences between frames. In addition, the authors use color features in terms of color coherence over time, high brightness and high saturation. The authors used the well-known C4.5 decision tree induction program to build a classifier for genre labeling. 
   Another technique for video categorization is described in
         N. Dimitrova, L. Agnihotri and G. Wei, “Video classification based on HMM using text and faces,” (Dimitrova et al.).       

   Herein, first fifteen labels defined based on these visual features (by text, the authors, mean superimposed text in the video) are defined, examples are “talking head” and “one text line.” A technique using Hidden Markov models (HMM) is described to classify a given media item into predefined categories, namely, commercial, news, sitcom and soap. An HMM takes these labels as input and has observation symbols as output. The system consists of two phases, a training and a classification stage. Reference Dimitrova et al. is incorporated herein in its entirety. 
   It is important to note that Dimitrova et al. does not use text in machine (ASCII) readable form, it uses the presence or absence of text block(s) in the video frames. 
   On the other hand, such machine-readable ASCII text, along with, visual features is used for video categorization in M. A. Smith and T. Kanade, “Video skimming for quick browsing based on audio and image characterization,” Carnegie Mellon University, Tech. Rep. CMU-CS-95-186, June 1995 (Smith et al.). 
   Reference Smith et al. is incorporated herein in its entirety. A sophisticated video database browsing systems is described, the authors refer to browsing as “skimming.” Much emphasis is placed on visual analysis for video interpretation and video summarization (the construction of two-dimensional depictions of the video to allow for nonlinear access). Visual analysis include scene break detection, camera motion analysis, and object detection (faces and superimposed text). The audio transcript is used to identify keywords in it. Term frequency inverse document frequency techniques are used to identify critical words. Words that appear frequently in a particular video segment but occur infrequently in standard corpuses receive the highest weight. In Smith et al. the speech recognition is not automated yet, and closed-captioning is used instead. Video search is accomplished through the use of the extracted words as search keys, browsing of video summaries then allows for quickly finding the video of interest. 
   A content-based video browsing system that applies linguistic analysis to the closed captioning is described in I. Mani, D. House, M. Maybury, M. Green, “Towards content-based browsing of broadcast news video,” in Intelligent Multimedia Info Retrieval, Issue 1997, M. T. Maybury (ed.), pp 241–258. AAAI Press/The MIT Press (Mani et al.). 
   The reference Mani et al. is incorporated herein in its entirety. 
   Emphasis in Mani et al. is placed on topic and story segmentation. Assuming that one could associate terms in a document with subjects in a thesaurus, the authors hypothesize that as topics change, the associated thesaural subjects change as well. The work is based on a thesaurus of 124 subject categories, with text summaries represented in a 124-dimensional space. Well-known subject similarity measures as the angle between subject vectors are used. The issue then is detecting a change in topic by detecting a change in angle. The subject vector, however, has to be computed over a certain video time interval, the authors refer to this as a block. The block size is important here. The authors do not arrive at a universally usable block size and contemplate adjustable block size. Further, the authors consider the use of cues that closed-captioners insert, in particular “&gt;&gt;” indicates a change of speaker, while “&gt;&gt;&gt;” indicates a change in topic. These cues were found to be unreliable. Therefore, the authors investigate the use of what they call “sign off” cues. These are specific textual cues that indicate a change in topic, as “Goodnight Jim” in the MacNeil-Lehrer NewsHour shown in the past on PBS. The authors use no visual cues to detect story boundaries. 
   Finally, the use of automated speech recognition of the audio track to determine story and topic is being used more and more since speech recognition technology is steadily improving. The use of automated speech recognition can be classified as (1) dictation applications, (2) conversational or transactional applications, and (3) indexing applications. A comprehensive and excellent overview of the latter application is presented in Coden et al.:
         A. R. Coden, E. W. Brown, “Speech Transcript Analysis for Automatic Search,” IBM Research Tech. Rep., IBM Research Tech. Rep., (Coden et al.).       

   This reference (Coden et al.) is incorporated herein by reference. All of the video indexing, video summarization, video segmentation, and video categorization and subject detection technologies based on automated speech recognition, described in Coden et al., use no or very little visual information. 
   There is also quite some prior art dealing with segmenting documents (discourse) into portions corresponding to topics. This is typically referred to as “discourse segmentation” to distinguish it from character segmentation from image or video for optical character recognition (OCR). The term “discourse,” further is more general because it includes spoken language, which is transcribed from wave forms to text (e.g., ASCII) for analysis purposes. In the following discussion, we will use the terms interchangeably. 
   One popular recurring idea is to partition the discourse into fragments, and to measure the “similarity” of one fragment to another, using the cosine metric, which is the dot product of the word occurrence frequencies. (Morphological analysis is usually employed first, to reduce inflected, declined, etc., words to their base forms—“stemming” or “lemmatization”). 
   Reference Hearst, M. A., Multi-paragraph segmentation of expository text. Proceedings of the 32nd Annual Conference of the Association for Computational Linguistics, Las Cruces, N.Mex., 1994, pp. 9–16. (Hearst), is incorporated herein by reference. 
   Hearst does this by partitioning the entire document into tiles of more or less uniform size, the size being on the order of a paragraph. She then plots C (j, j+1) versus j, for j=1, . . . , N−1, where N is the number of tiles in the document, and C is the inter-tile co-occurrence (or similarity) coefficient. After smoothing of this curve, local minimal values indicate discourse boundaries, since minimal similarity indicates probable different topics of the tiles. 
   Also incorporated by reference is J. C. Reynar, “An automated method of finding topic boundaries,” Proceedings of the 32nd Annual Conference of the Association for Computational Linguistics, student session, Las Cruces, N.Mex., 1994, pp. 331–333, (Reynar). Reynar divides a discourse at a very fine grain: the individual word. He then records the correspondences (0 or 1) with every other word in an N×N matrix, where N is the document size in words. Then any choice of discourse boundaries defines a set of square sub-matrices of the matrix lying along the main diagonal, each sub-matrix representing the intra-segment co-occurrence values. Reynar defines the best discourse segmentation to be the one that minimizes the density of 1&#39;s in the extra-segmental co-occurrence regions of the matrix. Here the extra-segmental regions are all matrix entries not lying in the intra-segmental sub-matrices. He calls his technique dotplotting. 
   Further references Ponte and Croft, and Kozima are incorporated herein by reference:
     Ponte J. M. and Croft W. B. 1997, Text Segmentation by Topic, in Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries, pp. 120–129. (Ponte and Croft)   Kozima, H. 1993 Text Segmentation based on similarity between words. In Proceedings of the 31st Annual Conference of the Association for Computational Linguistics, Columbus, Ohio. pp. 286–288, (Kozima)   

   Ponte and Croft, use a similar technique, except that they “expand” each word in a partition by looking it up in a “thesaurus” and taking all of the words in the same concept group that the seed word was in. (This is an attempt to overcome co-ocurrence, or correspondence, failures due to the use of synonyms or hypernyms, when really the same underlying concept is being referenced.) Ponte and Croft bootstrap the correspondences by developing a document-specific thesaurus, using “local context analysis” of labeled documents. Then, to find the best co-occurence sub-matrices, instead of exhaustively considering all possibilities, they use a dynamic programming technique, minimizing a cost function. Kozima et al. perform a similar word “expansion,” by means of “spreading activation” in a linguistic semantic net. Two words are considered to be co-occurrences of, or corresponding to, each other if and only if each can be reached from the other by less than m steps in the semantic net, for some arbitrarily chosen value of m. 
   There are feature-based approaches, too, that do not rely on word co-occurrence or correspondences, for example, Litman and Passoneau. Here a set of word features is developed. These features are derived from multiple knowledge sources: prosodic features, cue phrase features, noun phrase features, combined features. A decision tree, expressed in terms of these features, is then evaluated at each potential discourse segment boundary to decide if it is truly a discourse segmentation point or not. The decision expression can be hand-crafted or automatically produced by feeding training data to a learning system such as the well-known C4.5 decision tree classification scheme 
   Reference [Litman D. J. and Passoneau R. J. 1995. Combining multiple knowledge sources for discourse segmentation. In Proceedings of the 33rd Annual Conference of the Association for Computational Linguistics, Cambridge, Mass.], (Litman and Passoneau), is incorporated herein by reference. 
   Now consider [D. Beeferman, A. Berger and J. Lafferty, Text Segmentation Using Exponential Models, CMU Tech Rep], (Beeferman, et al.) that is incorporated herein by reference and introduces a feature-based discourse segmentation technique for documents. The idea is to assign to each position in the data stream a probability that a discourse boundary occurs. Central to the approach is a pair of tools: a short- and a long-range model of language. The short-term model is a trigram model, the conditional probability of a word based on the two preceding words. The long-term model is obtained by retaining a cache of recently seen trigrams. Determining a discourse boundary in statistical terms is cast by formulating the probability of a boundary both in terms of the short- and the long-term model. Maximal values of this probability then indicate discourse boundaries. Beeferman et al. touch upon, but do not implement, multimedia document (containing audio, text and video) discourse segmentation. Examples of short- long-term features that they propose are: “is there a sharp change in video stream in the last 20 frames,” “is there, a blank video frame nearby,” and “is the a match between the spectrum of the current image and the spectrum of the image near the last segment boundary.” 
   In sum, we can (roughly) distinguish the following approaches to media item categorization and media item subject detection; or, more generally, media item classification. The approaches are classified based on the features that are used. The features are derived from the raw analog signal, visual features computed from digitized media items frames (images), textual features directly decoded from the closed-caption, and textual features obtained from automatically computed speech transcripts. Here is a list of common kinds of features used to classify multimedia items:
         Raw analog visual and audio signals.   Visual features computed from individual frames.   Visual features computed from individual frames plus temporal features.   Visual features computed from individual frames, temporal features plus audio features.   Semantic visual features computed from individual frames plus temporal features.   Semantic visual features computed from multiple frames and temporal features.   Closed-captioning (predetermined keyword spotting) plus visual features.   Speech transcript (predetermined keyword spotting) plus visual features.   Using only textual data, either speech transcript or closed-captioning.   Speech transcript computed from audio track.   Speech transcript computed from audio track plus rudimentary visual features.   Text document analysis.       

   PROBLEMS WITH THE PRIOR ART 
   Some of the problems with the prior art are now presented.
     1. The prior art media item categorization techniques for business decisions are based either only on visual information or only on textual information.   2. The media item representations are not designed to handle both textual and visual features that can be extracted from media items. This inherently limits the number of media items that can be distinguished, i.e., the discrimination power of the representations will not extend beyond a certain (not very large) number of different video categories and the number of business decisions, consequently, is then smaller because of prior art limitations.   3. The media item representations are not designed to handle in a coherent fashion both textual and visual features that can be extracted from media items and hence business decisions cannot be made in a coherent fashion.   

   OBJECTS OF THE INVENTION 
   An object of this invention is an improved business system and method for categorizing multimedia items. 
   An object of this invention is an improved business system and method for categorizing multimedia items using both textual and visual features. 
   An object of this invention is an improved business system and method for categorizing multimedia items while handling both textual and visual features coherently. 
   An object of this invention is a business system for performing categorizing of multimedia items in a large number of categories (classes). 
   SUMMARY OF THE INVENTION 
   The present invention relates to business processes based on a categorization methodology for categorizing multimedia items. Examples include video and images with captions. There are two phases: a learning phase, whose purpose is to induce a classifier, and categorization phase, in which the classifier may be applied in business processes. 
   In the learning phase, off-line supervised or unsupervised learning, using a training set of labeled or unlabeled multimedia items as appropriate, is employed to induce a classifier based on patterns found in a unified representation as a single feature vector of disparate kinds of features, linguistic and visual, found in a media item under consideration. The unified representation of disparate features in a single feature vector will enable a classifier to make use of more complicated patterns for categorization, patterns that simultaneously involve linguistic and visual aspects of the media, resulting in superior performance as compared with other less sophisticated techniques. This allows for better designed and more precisily performing business processes. First, for each media item, the accompanying text is represented by a sparse textual feature vector. Secondly, for each media item, a set of key frames or key intervals (key intervals, for short) is determined, which can either be regularly sampled in time or based on the information content. From each key interval, a set of features is extracted from a number of regions in the key intervals. These regions can be different for each feature. The extracted features are coarsely quantized. Hence, each key interval is encoded by a sparse textual feature vector and a sparse visual feature vector. The sparse textual feature vectors and the sparse visual feature vectors may optionally need to be further transformed to assure their compatibility in various ways, such as (1) with respect to range of the values appearing in the two kinds of vectors or (2) with respect to the competitive sizes of the two kinds of vectors with respect to some norm or measure. The textual feature vector and the visual feature vector are combined by concatenation to produce a unified representation in a single vector of disparate kinds of features. Having created the unified representation of the training data, standard methods of classifier induction are then used, followed by appropriate business processes and business decisions. 
   In categorization phase, the process of computing sparse textual feature vectors and sparse visual feature vectors for a media item is repeated. The classifier induced in the learning phase is used to identify the class (category) of a media item and appropriate business processes and business decisions might also be learned. 
   A standard method of classifier induction is the construction of a k-nearest-neighbor classifier based on training data whose elements are labeled with the classes to which they belong. A k-nearest-neighbor classifier is one that classifies vectors using some measure of similarity. It assigns to a vector whose class is unknown the class to which a majority of its k nearest neighbors in the training data belong. The simplest kind of a k-nearest-neighbor classifier is one in which k is taken to be 1. The categorization phase for this simplest kind of k-nearest-neighbor classifier amounts to, for an item whose class is unknown, finding its nearest neighbor, which is the most similar item in the training data to that item according to a similarity measure, and assigning to the unknown item the class of its nearest neighbor. This invention explicitly includes the use of these fairly simple classifiers (as well as their more complex classifiers, such as support vector machines and various classifiers based on statistical analysis of the vectors representing the training data) among the standard methods of classifier induction to which reference was made above. In particular, this invention includes the solution of the problem of finding the media item in a reference collection that is most similar to a hitherto unconsidered media item by ascertaining the degree of similarity between the vector representations of the hitherto unconsidered media item and each reference media item, as long as the vector representations that are compared are constructed as described above, thereby providing a unified representation of disparate modalities of the media items being compared. Based on the multimedia item(s) further appropriate business processes and business decisions can be applied. 

   
     BRIEF DESCRIPTION OF THE FIGURES 
     The foregoing and other objects, aspects and advantages will be better understood from the following non limiting detailed description of preferred embodiments of the invention with reference to the drawings that include the following: 
       FIG. 1  is a prior art block diagram of the state-of-the-art categorization technology for document categorization. 
       FIG. 2  is a flowchart showing the disparate sources of information that are used for media item categorization 
       FIG. 3  is a more specific flowchart describing the combined computation of the disparate sources of information from a media item. 
       FIG. 4  is a detailed flowchart for inducing classifiers for categorizing media items that comprise of disparate sources of information. 
       FIG. 5  is an example of a decision rule induced by the training processes that combines visual and textual information. 
       FIG. 6A  is a block diagram of the learning/training phase of one preferred system embodying the present invention. 
       FIG. 6B  is a block diagram of the classification phase of one preferred system embodying the present invention. 
       FIG. 7A  is a flowchart of the visual feature extraction process for media items, where first media item regions are selected and then the data within the regions is transformed. 
       FIG. 7B  is a flow chart of a visual feature extraction process for media items, where first the media item is transformed and then regions in the feature domain are selected. 
       FIG. 8  is a sketch of an media item frame with two arbitrary regions from which the features are computed. 
       FIG. 9  is a video frame where the regions are rectangular windows (regions). 
       FIG. 9A  is an example video frame with rectangular regions that show the data that is used for feature computation. 
       FIG. 10  shows a flow diagram of a video transformation where the regions span one or more frames and the media item visual feature is optical flow. 
       FIG. 11A  is an example flow diagram of visual hue feature computation for a key frame with rectangular windows. 
       FIG. 11B  gives an example quantization of hue space to define visual hue codes. 
       FIG. 12  is a flowchart of the visual feature vector computation from the visual part of the media item. 
       FIG. 13A  shows a method for computing visual feature vectors from the visual part of the media item where both temporal and spatial properties of the media item are preserved. 
       FIG. 13B  shows a method for computing visual feature vectors from the visual part of the media item where only the temporal properties of the media item are preserved. 
       FIG. 13C  shows a method for computing visual feature vectors from the visual part of the media item where only the spatial properties of the media item are preserved. 
       FIG. 13D  shows a preferred specific method for computing visual feature vectors from the visual part of the media item where the key intervals of the media item are ordered based on one or more features of the key intervals. 
       FIG. 14A  is a flowchart of the process for computing a first visual feature vector in  FIG. 13D . 
       FIG. 14B  is a flowchart of the process of shortening the first visual feature vector to a standard length vector in  FIG. 13D . 
       FIG. 15  shows the process of combining visual and textual feature vectors to obtain a vector representing the disparate sources of information in the media item. 
       FIG. 16  is a detailed system block diagram of the categorization system, illustrating how different formats of media items are handled in the categorization process. 
       FIG. 17  introduces the idea of associating a feature vector with a continuous portion of the media item as opposed to associating the feature vector with the entire media item. 
       FIG. 18  is a flowchart of a system that uses the media item classifier for segmenting a temporal media item into contiguous segments, where each segment is of one or more categories. 
       FIG. 19  is a one-dimensional depiction of the categorization as a function of time function that points out problems with temporal media item categorization. 
       FIG. 20A  gives heuristic aggregation rules based on category boundaries. 
       FIG. 20B  gives heuristic aggregation rules based on category regions. 
       FIG. 21A  is an instance of the flowchart of  FIG. 18  where the optimal category aggregation is determined by optimizing a cost function. 
       FIG. 21B  is a system for learning aggregation rules based on labeled training data and optional heuristic rules. 
       FIG. 22  shows two examples of a lowest-cost interpretation rules and the application of the rules to a short sequence of categories. 
       FIG. 23  is a business method for determining profiles of those multimedia item categories of particular interest to various different users retrieving multimedia content from a private and/or public network. 
       FIG. 24A  is a business application of the multimedia item categorizer for building index tables to organize a multimedia item data base in terms of categories or indices. 
       FIG. 24B  is a business application for retrieving of multimedia items from database based on the database organization and database indexing described in  FIG. 24A . 
       FIG. 25  is a business application where one or more of multimedia items are received and the items are routed according to the multimedia items categories. 
       FIG. 26  is a business application where one or more multimedia items are received and for each item a decision based on the category of the multimedia item is made. 
       FIG. 27  is a business application where one or more multimedia items on a private and/or public network are examined and for each found multimedia item a decision based on the category of the multimedia item is made. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   This system categorizing media items has two distinct aspects. The first aspect is called the training phase which builds representations of the reference media items; the second phase is called the categorization phase, where instances media items are categorized. The training phase is an off-line process that involves processing of the reference media items to form a set of one or more categories. The categorization phase classifies a media item in a collection of such items by processing the media item to extract audio and visual features and using the media item class representations. 
     FIG. 1  shows a prior art flowchart for a system  100  for categorizing text documents. In step  110 , a set of text documents is input to the system. Each text document is labeled as belong to a class S=c 1 , i=1, . . . , C. The classes S can be hierarchical, in the sense that each class S, can be recursively divided up into a number of subclasses, S=S subclass1 , S subclass2 , . . . , S subclassN . In  120  a single vector is computing representing the text in each document in D. Such a vector V is a large-dimensional vector with entry n equal to 1 or 0, respectively, if word n is present, or not, in the document; or, such a vector V can be a large-dimensional vector with entry n equal to f where f is the number of times word n is present in the document. Examples of source of text vectors  120  include: close captions, open captions, captions, speech recognition applied to one or more audio input, semantic meanings derived from one or more audio streams, and global text information associated with the media item. In step  130  ( FIG. 1 ) each vector V is labeled the same as the corresponding document. Step  140  induces machine-learned classification methods for classifying unseen vectors  V  representing new unclassified documents. Finally, Box  150 , infers classification method to classify (categorize) unknown documents  D , represented by feature vectors  V . 
     FIG. 2  shows a diagram of the various disparate input sources to the categorization process. The general multimedia input sources is media item  200 . This input source consists of audio  210 , text documents  220  and visual input  230 . Using the audio input, a speech transcript in computed ( 240 ) applying well-known techniques in the prior art. Possibly, if needed and available, a closed- or open-captioning ( 220 ) of the visual footage ( 230 ) is used to generate textual features  120  (very much in line with step  120  in  FIG. 1 ). Thirdly, the visual track  230  is transformed into characteristic visual feature spaces ( 250 ). The current invention puts these disparate sources of information, i.e., speech transcript representation  240 , closed-caption representation (general textual features)  120 , and visual features transformed into characteristic spaces  250  together in a unified framework. This framework allows the treatment of these sources of information in a coherent fashion. 
     FIG. 3  shows a more specific way of processing the multimedia media item for categorization purposes. The input data, media item ( 300 ), is processed separately in terms of the visual track,  305 , and the audio track,  310 . The visual track  305  and the audio track are processed independently and concurrently. From the visual track ( 305 ), characteristic key frames or key intervals are selected  320 . These characteristic pieces of video are transformed into characteristic visual spaces  330  that in some way characterize the video clip in terms of visual features associated with the video categories. These visual space representations are transformed into sparse visual feature vectors ( 335 ). The audio track  310  is automatically transcribed into a precise as possible transcript. Additionally, possible available closed- and open-captioning is included in the textual description of the media item  300 . In  360  the textual content associated with the current media item  330  is tokenized (i.e., the sequence of characters in the text is divided into words and other meaningful tokens) and, optionally, stemmed (i.e., tokens are replaced by standard forms, roots, or morphological stems in order to reduce the size of the feature space ultimately constructed, such as replacing the word cats by cat, the word men by man, the work came by come, etc. Step  365  computes a textual representation vector of the current media item  300  under consideration. In output  370 , a collective coherent textual and visual representation of the media item is accomplished established. An out put vector  380  is the result of this process. 
     FIG. 4 , flowchart  400 , shows, when supervised learning is employed, the complete integration of disparate media modules in the learning phase, i.e., the induction from labeled data of a classifier whose purpose is media item categorization. In the initial step  410 , the system accepts as input a data set D consisting of media items, each labeled as belonging to 0 or more classes from a set or hierarchy of classes S. Steps  420  and  430  may be permuted or carried out simultaneously. In  420 , the system constructs a single vector representation of text features and/or audio features extracted or associated with each media item in D. These features may be present in a transcript produced by voice recognition software, in close-captioned text, or in open-captioned text. Some features may indicate the presence of or the character, appropriately quantized, of other audible characteristics of the media segment, such as music, silence, and loud noises. In  430 , the system constructs a single vector representation of the visual features extracted or associated with each media item in D. In  440 , for each labeled media item in the data set D, the system constructs a training set T(D) by combining the two vector representations of that media segment (constructed in  420  and  430 ) into a single composite feature vector, with the resulting vector labeled by the same set of classes used to label the media item. Optionally, in  440 , before combining the vector representations, the system may uniformly transform one or both of those set of representations in order to assure compatibility. Among the ways that incompatibility may arise may be (1) a marked difference in the number of values that may appear as components of the vectors and (2) a marked difference in the norms or sizes of the vectors present in the two sets. The exact criterion for what constitutes a marked difference between to the sets of vectors will depend in practice on the particular technique of supervised learning being employed, and it may idiosyncratically depend on the data set D. Thus, in practice, the criterion may be experimentally determined by the evaluation of different classifiers induced under different assumptions. At any rate, in  440 , the system ultimately produces, normally by concatenation of the (possibly transformed) feature vectors produced in  420  and  430 , a composite labeled feature vector is ultimately produced for each media item in D. In  450 , the system uses a supervised learning technique—a wide variety of them exist—with T(D) as training data to induce a classifier that can be used to assign classes in S to a hitherto unseen feature vector with the same structure as those in T(D). In  460 , the system outputs the classifier induced in  450 , as well as any parameters, information or settings needed to represent hitherto unseen media items as unified feature vectors with exactly the same format and structure as those in T(D), so that the classifier induced in  450  can be legitimately applied to those vectors. 
     FIG. 5  shows a typical example of video classification rules derived from disparate information sources such as the visual and audio track. These sources are visual,  510  and  516 , and auditory,  512  and  514 . In the speech track the words “golf” and “grass” ( 512  &amp;  514 ) are detected. The combination of these two words point  520  point to the fact that we are dealing with a piece of video footage of category sports  525 , with the subject golf  530 . However, the visual circumstances in the topic of golf  510 , where the actual game of golf is played, and a relatively static scene  516  of a “talking head” indicating a golf interview. Items  510  and  520  together infer that the medium items has category “sports”  545  with subject “golf game”  548 . On the other hand, when the footage is relatively static,  516 , it is clear that the category of the footage is sports again “ 555 ” but that the subjects is an interview about golf “ 558 .” 
     FIG. 6  shows a block diagram of the system architecture. The top portion ( FIG. 6A ) shows the supervised/unsupervised phase  610 , i.e., the part of the system that handles the computation of classes and learns a representation (i.e., class/category representations  675 ) of the reference media items  610 . The learning is achieved through training engine  620 , which computes the class representations  675 . The bottom portion ( FIG. 6B ) shows the classification/categorization phase  615  using the classification/categorization engine  650 . Here features are computed from the target incoming medium item M  660 . The class/category of medium item  660  is reported to  685 . Each of these phases is described in detail below. 
   The training phase, as shown in  FIG. 6A , includes the key frame or key interval (hereafter referred to as key interval) selection step, the visual feature generation step, and the visual feature vector population step. Each of these steps is discussed below. 
   Visual features  775  are computed from key intervals. Two methods for visual feature computation are described in  FIGS. 7A and 7B . The media item  750  in this case has a time dimension  755 . Characteristic key frames, e.g., a frame a t 0    760 , or characteristic key intervals  770  are selected. A key interval could be a window [delta t1 , delta t2 ] around frame a t 0    760 . So far, the methods described in  FIGS. 7A and 7B  are the same. Key intervals  770  or key frames  760  are also selected in the same fashion for both methods. These key frames or intervals could be in the middle of shots, they could be at the beginning and end of shots, or the could be equally spaced over the shots. Alternatively, the key intervals can be selected based on visual properties of the media item, such as, temporal regions of minimal motion or temporal regions of maximal motion. 
   In  FIG. 7A , the visual feature computation  705  proceeds as follows. Given the key frame or key interval  770 , this interval is quantized into regions in step  706 . In step  707 , the data in each region are transformed into a different feature space. (Such a transformation could, for example, be visual flow computation for each region.) Following this, in step  708 , region features are computed. (In the visual flow example this could be average flow in a region.) The visual features  775  are output of visual feature computation process  705 . In  FIG. 7A , the visual feature computation  705  proceeds differently. Now, in step  717  first the data in the key frame or key interval is transformed into a different feature space. (Such a transformation could, for instance, be a Fourier transform.) In step  716 , the values of this transformation are regarded as a domain and this domain is quantized into regions. (If the transformation is the Fourier transform, the domain is the frequency spectrum.) For each of the regions in that domain, in step  708 , region features are computed. The visual features  775  are output of visual feature computation process  705 . The visual features are coarsely quantized into codes (see, for example,  FIG. 11B ) and these codes are used to populate feature vectors as in  FIG. 13 . 
   The feature transformation and feature extraction steps are dependent on the type of similarity to be measured. For example, image color based coding processes have been discussed in Smith et al. There are several other techniques of media transformation and feature computation that are well known in the art. See Smith and Chang, Tools and Techniques for Color Image Retrieval, In IS&amp;T/SPIE Proc Vol. 2670, Storage and Retrieval for Image and Video Databases. 
   The feature-based vector generation process is applied to all the key intervals selected by the key-framing process. The media item M in  FIGS. 7A  and B is depicted by a horizontal rectangle  750 . 
   Time running from time 0 to time T seconds is indicated by the time axis  755 . A key frame is selected at time is t 0  ( 760 ) or, alternatively, a key interval ( 770 ) is selected between times delta t1  and delta t2 . 
   In general, the visual feature computation is comprised of a number of steps. The complexity of each of the steps is dependent on the feature being considered. In this invention, for media items we describe in more or less detail two visual features, namely, image color-based codes and optical flow-based codes. As indicated above, this feature code generation process, that generates feature values  775 , is comprised of two steps, namely, the step where features are extracted  705  and a quantization or code generation step as in  FIG. 13 . Each of these steps is discussed below. 
   Referring again to  FIGS. 7A  and B, in the feature vector computation step  705 , the data in the key intervals are processed to extract different types of measurements or visual features. These measurements may be based on both global and local spatial or spatio-temporal properties of a key interval. These measurements may also be extracted from predetermined portions (regions) of the key intervals. 
   For example, in  FIG. 8  a part of a media item  810  (e.g., key interval) is shown with two arbitrary shaped regions,  820  and  830 , region (1) and region (2), respectively. Only from the visual data in these regions, the feature values  775  for (say) feature F j  are computed. The regions,  820  . . .  830 , here can cover the complete key interval and the regions can differ the features F j , j=1, 2, . . . that are used. Each feature F j  may have different regions W j  and a different number of these local regions. 
     FIG. 9  shows an example where the media item key frames  900  for feature F j  are divided up into 16 rectangular windows (regions)  901 ,  902 , . . . ,  903 , . . . ,  904  (the local regions). Other windowing schemes are easily contemplated by persons skilled in the art. 
     FIG. 9A  gives an example of a video frame  950  (part of the media item) with the window structure  901 ,  902 , . . . ,  903 , . . . ,  904  (as in  FIG. 9 ) overlaid. Each window  960  contains data which is used for visual feature vector computation; the media transformation  705  would use data in the whole frame  950 . In the case where the domain regions are windows, with w 1  windows horizontally and w 2  windows vertically we have W j =w 1 ×w 2  regions. The media item region selection ( 706 ) for each visual feature j which has W j  quantized elements. These features are then stored in feature vector  755 . 
   Referring to  FIG. 7A , media regions are selected  706  every time t 0  ( 760 ), i.e., a key interval, is encountered. Each feature F j , j=1, . . . , m is computed from the data in the appropriate regions for he different features F j . The region transformation  707  depicts the transformation of media into feature space, such features can be color space transformation, edge detection, optical flow, etc. In general, the resulting feature values are not directly used as input to the feature range quantization step ( 715 ). Rather, an intermediate transform  708  is applied that maps the features obtained by region transformation  707  into a smaller number of feature values for each F j , j=1, . . . , m. An example is to compute the average hue for the regions  901 ,  902 , . . . ,  903 , . . . ,  904  in  FIG. 9 . Or, it maps a number of high-contrast edge element located in the form of video text into the coordinates of the window that contains the edge elements. In essence, the transformation  708  is a data reduction step so that the vector representations for the media items S=s 1 , . . . , s n  are as sparse as possible and yet the reference streams can still be distinguished. 
   Referring to  FIG. 7B , it is alternatively possible that all the data in the key interval can be transformed through a media item transform  717  into a feature space G. Hence, a mapping  717  is performed of all the data in a key interval into a feature space G. (An example here is a Fourier transform of the data in the key interval, with G one- or two-dimensional frequency spaces.) Then local regions of feature space G (feature regions), obtained through feature region selection  716 , are used as input to the region feature computation step  708 . 
   An example of this case is the computation of features from the audio track. The key (time) interval now could be [delta t1 , delta t2 ] ( 770  in  FIG. 7B ) and one of the features F can be the Fourier transform of this time interval. This gives the distribution of audio frequencies, from low to high, in the time interval [delta t1 , delta t2 ]. Regions in the domain [0,f max ) of G of the frequency distribution function are used to compute audio features (rather than the domain being the key interval). Here f max  is the maximum frequency in the key interval. For instance, one could use a subdivision of [0, f max ) into equal sub intervals (regions), and the output of transformation  708  (region-based-feature-computation) could be simply the average value or energy in the sub intervals. 
   The visual feature extraction can encompass many different transformations and quantizations. In the case of visual media, it includes, computing the color histogram of a key frame, computing color histogram of selected spatial regions in the key frame, computing the pixel difference between two or more temporally displaced key frames, computing a measure to detect the presence of high-contrast regions in a key frame (like scene-text), or computing the optical flow or other spatial displacements between two subsequent frames (or possible frames spaced further apart) within a key interval.  FIG. 10  shows this last case. A video stream  1010  is the input to a video transformation  1020  that computes the optical flow  1030  (transformed media). A possible way to implement this is to select key intervals of frames  1002  . . .  1008  in video stream  1010  as input to the video transformation  1020 . Each key interval is transformed into a feature domain which in this case are individual frames  1032  . . .  1038  of optical flow. Every pixel in these frame contains two values, x and y, that together represent the optical flow at the pixel in vector form (x,y) t . The optical flow is computed by comparing and matching two or more consecutive frames in the key intervals. Optical flow computation is well known in the prior art. Besides optical flow, any other transformation that has as domain two or more frames, be it consecutive frames or frames spaced apart, and maps this data into a function on some other domain that can be used as features for categorizing video sequences. 
   To put it simply, the domain is the data space from which the visual features are computed. The domain can be the video frame itself, which amounts to the red, green and blue channels; or, e.g., luminance. On the other hand, the domain could be the a functional transformation of two or more frames into a different domain, as optical flow between the frames. In that case, the domain is space of optical flow from which the visual features are computed. 
   Categorization segments of a media item M, is aided by using some color quantization, for example, the following frame color codes. The color space of frames (images) has been extensively used for indexing and searching based on image content. Application of hue color code, a preferred embodiment of this invention, is comprised of a number of steps (see  FIG. 11A ). 
   Color is a good feature for video categorization. In particular, the hue component of color contains much information. Hue is the portion of color information that indicates which color a particular pixel in frame  1110  has. The colors range from red, green to magenta (see  FIG. 11B .) Hence, from the video signal, which is as input to the system is typically in an RGB or YIQ format, the hue component has to be extracted. This requires a transformation from the original color space (RGB or YIQ) of the media item to the HSV color space. This is achieved by using the standard algorithms  1115  for color space conversions (e.g., Foley &amp; van Dam, Chapter 13). Once the image is transformed in to the HSV model, the hue channel is separated from the HSV model, as the code generation is based on the hue values of the pixels in the key frames. 
   Refer now to the block diagram of  FIG. 11A . Let media item  750  be the input and frame  1110  at time t 0    760  be the current key frame. This frame  1110  could be in YIQ format and is denoted as YIQ(frame) in  FIG. 11A . Then in block  1175  the feature (hue) vectors  1155  for this frame are computed. The regions for the frame are rectangular windows  1130 . The output of process  1175  therefore is a vector F=(f 11 , f 12 , . . . , f 1n , f i1 , f i2 , . . . , f in , f N1 , f N2 , . . . , f Nn ) with N the number of windows and n the number of color tokens per window. 
   The first step in  1175  of  FIG. 11A , is to convert the the YIQ encoded frame into an HSV encoded frame  1115 . This is well known in the prior art. The output then of  1115  is the hue part of the color information of the frame, denoted as Hue(frame)  1120 . In  FIG. 8A  the frame is divided up into windows  1130 . (Note that these need not be rectangular, they can be arbitrarily shaped as show in  FIGS. 8 &amp; 8A . Rectangles are a specific case of an arbitrary shaped window.) Block  1130  outputs Hue(window)  1135  for each window  901 ,  902 , . . . ,  903 , . . . ,  904  in the current frame. Subsequently, block  1140  determines the average hue, Average — hue(window), in the windows. The averaging is the first data reduction step. (Note that other averaging methods are contemplated. For example in one embodiment, the median of the window instead of the average is used and it is more robust to noise.) The second data reduction step in  1175 , is block  1150  which quantizes Average — hue(window) into a small number of quantization levels per window and assigns codes to each quantization level. The mechanics of this quantization and code assignment is explained in  FIG. 11B . As noted above, the output of step  1175  is a feature vector F=(f 11 , f 12 , . . . , f 1n , f i1 , f i2 , . . . , f in , f N1 , f N2 , . . . , f Nn )  1155 . 
   There are a number of different ways of extracting feature values Average — hue(window)  1145 . For example, at one extreme case the hue value at each pixel can be considered as a feature (i.e., the windows are the pixels) or at the other extreme the hue value of all the pixels in the frame can be averaged to generate the feature value (i.e., the window is the frame). In a preferred embodiment, as indicated above, the frame is divided into w 1  windows in one dimension of the image and w 2  windows along the other dimension as in  FIG. 9 . An average hue value is computed based on the pixels in each window. Thus the hue color for a video key frame is a set of w 1 ×w 2  average hue values. Quantized these give a feature vector C for each frame. 
   In  FIG. 11B ,  1180  is the hue value of a pixel. The hue values can range from 0 degrees to 360 degrees, in  FIG. 11B  indicated by  1160  (0 degrees),  1161  (30 degrees),  1162  (60 degrees), . . . ,  1163  (180 degrees), . . . ,  1164  (330 degrees),  1165  (360 degrees). The hue values from 330 degrees ( 1164 ) through 30 degrees ( 1161 ) are centered around the color pure red ( 1170 ), from 30 degrees ( 1161 ) through 90 degrees around the color pure yellow ( 1171 ) etc. To arrive at hue feature vectors  1155 , the hue value outputs of the averaging operation  1140  need to be coarsely quantized. This quantization, and code assignment, to obtain feature vectors  1155  is performed in step  1150 .  FIG. 11B  gives a possible hue quantization, code assignment, table  1180 . Coding could be performed according to the following table 
   
     
       
         
             
             
             
             
           
             
                 
             
             
                 
                 
                 
               Number in 
             
             
               Color range 
               Code 
               Color 
               Figure 
             
             
                 
             
           
          
             
               330 &lt; Average — hue (window) &lt;= 30  
               0 
               Red 
               1170 
             
             
               30 &lt; Average — hue (window) &lt;= 90 
               1 
               Yellow 
               1171 
             
             
                90 &lt; Average — hue (window) &lt;= 150 
               2 
               Green 
               1172 
             
             
               150 &lt; Average — hue (window) &lt;= 210 
               3 
               Cyan 
               1173 
             
             
               210 &lt; Average — hue (window) &lt;= 270 
               4 
               Blue 
               1174 
             
             
               270 &lt; Average — hue (window) &lt;= 330 
               5 
               Magenta 
               1175 
             
             
                 
             
          
         
       
     
   
   There are several different ways in which a feature can be quantized. The choice of quantization can affect the categorization processes. 
   The feature-based code generation steps discussed above have been separated out as steps for clarity of presentation. However, these steps are combined to minimize the computation required to generate these feature vectors. 
   The feature vector extraction and coding process described above is one specific method of generating the feature vectors. Depending on the kind of similarity metric being used, the feature extraction and coding process can be significantly different. The vector representation mechanism and its efficiency in performing media item categorization are not significantly affected by the coding scheme itself. For example, one possible metric of similarity is the motion similarity of image sequences, that is, here video sequences are compared based on flow rather than color. Such a coding and similarity measurement scheme can be used within the frame work proposed in this invention (see R. Mohan, “Video Sequence Matching”, cited above). 
     FIG. 12  is a flowchart  1200  of the process of the visual feature vector computation from the visual part of the media item. Input to this process is the visual part of the media item  1210 . Following this step is the spatial and temporal quantization  1220  of the visual part in the media item. The temporal quantization is the process of selecting key frames or key intervals as in  FIGS. 7A and 7B . The spatial quantization is the process described in  FIGS. 8 and 9 . From this spatio-temporally (both in image/frame space and in time) quantized visual data, features are computed  1230 , such as flow as in  FIG. 10  and hue as in  FIG. 11 . In step  1240 , these feature values are coarsely quantized  1240  into a small number of discrete values, or codes (e.g., the quantization  1180  of the hue values in  FIG. 11B ). A process of mapping or counting  1250  (described in  FIG. 13 ) derives a visual feature vector, F v,    1260 . 
   Moving on to  FIG. 13 , here three possible methods for constructing visual feature vectors F v  are given. The visual part of the media item  1310  consists in this figure of key frames or key intervals  1303 ,  1306 , and  1309 . Let N be the number of key frames or key intervals, W the number of windows (regions) per key frame/interval, and C the number of codes for a visual feature. 
   A first method to determine a visual feature vector is given in  FIG. 13A . The visual feature vector  1320  here is determined by a mappings  1325 ,  1327  from quantized feature values c 1 , c 2 , c 3  of key intervals to the feature vector  1320 . The coded feature values c 1 , c 2 , c 3  are mapped  1325  to entries of the visual feature vector  1320 . In that case, for a specific feature, the visual feature vector will be of length W×N, where W is the number of regions per key interval and N is the number of key intervals. Alternatively, the codes c 1 , c 2 , c 3  could represent the absence ‘c=0’ or presence ‘c=1’ of a certain feature; or, the codes c 1 , c 2 , c 3  could represent the absence ‘c=0’ or the amount of presence ‘c=x’, with 1≦x≦C where C is some upper bound. An example of the former could be the color feature red, an example of the latter could be a visual motion feature. Note that the length of the visual feature vector F v  depends on the number of key intervals N, the number of regions W, and the number of codes C. That is, the length is N×W. Both spatial and temporal information about the key interval is preserved in the visual feature vector F v . 
   A second method to determine a visual feature vector is given in  FIG. 13B . The visual feature vector  1330  here is determined by a counting  1335 ,  1337  the occurrences of feature code values in the key intervals. Key interval  1303  has codes c=1, 2, . . . , 6 (in this case the number of codes C is 6) associated with each of the spatial regions. The first 6 elements of the visual feature vector F v    1330  are determined by counting the codes in the first key interval  1303 . Code  1  is associated with 8 spatial regions, hence F v ( 1 )=8, code  2  is associated with 4 spatial regions, hence F v ( 2 )=4, and so forth. The next 6 elements of the visual feature vector F v    1330  are determined by counting  1337  the codes in the second key interval  1306 . This process is repeated for all key intervals and all visual features. Note that again the length of the visual feature vector F v  depends on the number of key intervals N but not on the number regions W. In particular, the length of the visual feature vector is N×C. Also note that the information of the spatial arrangement of the codes in the key intervals is lost. 
   A third method to determine a visual feature vector is given in  FIG. 13C . The visual feature vector  1340  . . .  1350  here is again determined by a counting  1345 ,  1355  the occurrences of feature code values in the key intervals  1303 ,  1306 ,  1309 , . . . . In this case, the counting  1345 ,  1355  is performed by determining the number of times a code occurs over corresponding regions in the key intervals. The first part  1340  of the visual feature vector F v  is determined by the first region W( 1 , 1 ) (the upper-left window) of the key intervals. The first element of the visual feature vector is the number of times that c=1 appears in the regions W( 1 , 1 ), in this case 2 times. The second element of the visual feature vector is the number of times that c=2 appears in the regions W( 1 , 1 ), in this case 0 times. This counting is done for all feature codes c=1, . . . , C, where C is the largest code number. The second part  1350  of the visual feature vector F v  is determined by the regions W( 1 , 2 ) (second region in first row) of the key intervals. The first element of this part  1350  of the visual feature vector is the number of times that c=1 appears in W( 1 , 2 ), in this case 2 times. The second element of the visual feature vector is the number of times that c=2 appears in W( 1 , 2 ), in this case 1 times. This counting is done for all feature codes c=1, . . . , C, where C is the largest code number. Note that the length of the visual feature vector for this particular visual feature is now equal to W×C. Also note that for this type of feature code occurrence counting the temporal information is lost. 
   The methods in  FIGS. 13A and 13B  preserve temporal information and, therefore, the length of vector F v  depends on N. For the method in  FIG. 13C , the length of vector F v  does not depend on N but the spatial information is lost. It is desirable that the length of vector F v  does not depend on N; that the temporal information is discarded; and, that the spatial information is maintained. 
     FIG. 13D  shows a preferred specific methods for determining a visual feature vectors F v  from the visual part of the media item  1310 . The key frames or key intervals  1356 ,  1353 ,  1360 , . . . ,  1359  are selected from a media item. However, by rearranging these key frames or intervals,  1383 ,  1386 , etc., these key frames or intervals are ordered into a new sequence  1320  of key intervals  1353 ,  1356 ,  1359 , . . . ,  1360  according to the value of visual feature that is to be encoded in the visual feature vector F v . For example, the visual feature of average frame brightness can be ordered from high to low in the sequence  1320 . Or, the visual feature of average optical flow in key intervals can be ordered from high to low in  1320 . The effect and purpose of this ordering is that the temporal information in the video stream is discarded. (This is analogous to discarding word location information in using word frequency vectors, e.g., F t , in text document analysis.) The visual feature value codes (quantized feature values) in the regions of the key frames or intervals are then mapped  1365  into a first visual feature vector  F   v    1370 . Assume that there are n key frames or intervals in media item  1310  with W regions per key frame this gives a feature vector  F   v    1370  of length N=n×W. So we have the first ordered key frame or interval  1353  through the n-th ordered key frame or interval  1360 . Code c 11  in region W( 1 , 1 ) of the first key interval  1353  is mapped to the first element of  F   v    1370 , code c 21  in region W( 1 , 1 ) of the second key interval  1356  is mapped to the second element of  F   v    1370 , code c 31  in region W( 1 , 1 ) of the third key interval  1359  is mapped to the third element of  F   v    1370 , and code c n1  in region W( 1 , 1 ) of the n-th region  1360  is mapped to the n-th element of  F   v    1370 . Then, code c 12  in region W( 1 , 2 ) of the first key interval  1353  is mapped to the (n+1)-th element of  F   v    1370 , code c 22  in region W( 1 , 2 ) of the second key interval  1356  is mapped to the (n+2)-th element of  F   v    1370 , code c 32  in region W( 1 , 2 ) of the third key interval  1359  is mapped to the (n+3)-th element of  F   v    1370 , and code c N2  in region W( 1 , 2 ) of the n-th key interval  1360  is mapped to the 2n-th element of  F   v    1370 . This process is repeated till all codes in all key intervals are mapped into vector  F   v    1370 . Note that the length  1367  of the resulting visual feature vector  1370   F   v  is N=n×W ( 1371 ). This process is further described in  FIG. 14A . 
   The last step in  FIG. 13D  is to map  1375  visual feature vector  1370   F   v  to a fixed-length visual feature vector F v    1380 . The length of this vector is M, F v =(k 1 , k 2 , . . . , k M ). We require here that M=m s ×W&lt;N=n×W. 
   There two preferred methods to achieve this, sub sampling vector  F   v  and averaging components of  F   v . These are described below and as process  1440  and process  1470  of  FIG. 14B , respectively. For sub sampling m s =m c , for averaging, m s =m f . 
   A first method is sub-sampling  F   v . Let m c =┌N/M┐, the ceiling of N divided by M, the sampling factor of vector  F   v  to obtain F v . The constant M should be an integer multiple of W, let m=M/W. Sub sampling this vector  F   v  is achieved as follows. Let  F   v =( R ( 1 ),  R ( 2 ), . . . ,  R (W)), where each  R (i), i=1, . . . , W,  R (i)=( r (i,  1 ),  r (i,  2 ),  r (i,  3 ), . . . ,  r (i, n)) of length n represents a key interval region W(i). Then  F   v =( r ( 1 , 1 ),  r ( 1 , 2 ), . . . ,  r ( 1 ,n), . . . ,  r (i, 1 ), . . . ,  r (i,n),  r (W, 1 ), . . . ,  r (W,n)). The vector F v  is then (R( 1 ), R( 2 ), . . . , R(W)), where each R(i) is obtained by sub-sampling  R (i). That is, R(i)=(r(i, 1 ), r(i, 1 +m c ), . . . , r(i,m)). This is further explained as process  1440  in  FIG. 14B . 
   A second method to shorten  F   v  is to average components in  F   v  vector to obtain a vector F v  of fixed length M. Let m f =└N/M┘, the floor of N divided by M, the number of components of vector  F   v  to obtain F v . The constant M is again an integer multiple of W, let m=M/W. Again,  F   v =( R ( 1 ),  R ( 2 ), . . . ,  R (W)), with each  R (i), i=1, . . . , W, representing a region in a key interval. That is,  R ( 1 ) represents the first region W( 1 , 1 ), also denoted as W( 1 ), an so on. The vector components of  F   v  are  R (i)=( r (i, 1 ),  r (i,  2 ),  r (i,  3 ), . . . ,  r (i, n)) of length n represent a region W(i) of the key intervals. Let the shortened vector F v =(R( 1 ), R( 2 ), . . . , R(W)), with R(i)=(r(i, 1 ), r(i,  2 ), r(i, 3), . . . , r(i, m)). Then each vector  R (i) is mapped into a corresponding shorter vector R(i) as:
 
 R ( i )=( r ( i , 1 ), . . . ,  r ( i, m )); with  r ( i, j )=avg[ r ( i , ( j− 1) m   f +1),   r   ( i ,( j− 1) m   f +2), . . . ,    r   ( i ,( j− 1) m   f   +m )],
 
   This shortening of the vector  F   v  to the vector F v  is described in the flowchart process  1470  of  FIG. 14B . 
   In  FIG. 14A , is a flowchart of the process for computing the first visual feature vector  F   v    1370  from the media item  1310  is shown. Input to this process is media item  1310  and in step  1401 , key frames or key intervals are selected. (This process is described in  FIGS. 7A  and B.) The output is a set of key intervals labeled k i , i=1, . . . , n ( 1402 ). The number of key intervals n depends on the length of the media item. Process  1403  sorts these key intervals based on a visual property. As noted above, the visual feature of average frame brightness can be ordered from high to low  1380 . Or, the visual feature of average optical flow in key intervals can be ordered from high to low  1380 . The effect and purpose of this ordering is tat the temporal information in the video stream is discarded. (This is analogous to discarding word location information in using word frequency vectors, e.g., F t , in text document analysis.) The output  1320  of process  1403  is a set of key intervals labeled k j , j=1, . . . , n ( 1320 ). This is the set of n key intervals,  1353 ,  1356 ,  1359 , . . . ,  1360  of  FIG. 13D . The process, then, of populating the first visual feature  F   v    1370  with the elements c ij  of the ordered key intervals  1353 ,  1356 ,  1359 , . . . starts at step  1410 . First, in step  1412 , the pointers i, j, k, are set to 1 i=j=k=1. In step  1414 ,  F   v  (k) is set to c ij . Subsequently, both i and k are incremented in  1416 . The next step is to check if i&gt;n, if not  1418 , the next element of  F   v  (k) is set to the element c ij  of the next key interval. If i&gt;n  1420 , j is incremented  1422 , meaning that the next window of the key intervals will be handled. A test is performed first to determine if j&gt;W. If not  1424 , on the other hand, the c ij  of the next window are entered into visual feature vector  F   v . if yes  1426 , this means that all window visual feature vectors are entered into  F   v  and the process ends  1428 . 
   The flowcharts in  FIG. 14B  describe two shortening processes  4132  of the vector  F   v    1430  to the vector F v    1434 . Two processes for shortening the vector  F   v    1430  are described in this figure, the process of sub sampling  F   v    1440  (on the left) and the process of averaging  F   v    1470  (on the right). 
   The process  1440  starts at step  1441 . In step  1442 , the variables W (the number of regions per key interval), N (length of F v ), M (length of F v ), m c =┌N/M┐ (the sub sample rate), m=M/W, and the vector  F   v  are input to the system. Additionally, the variables i, w, s are set to one, i=w=s=1. In step  1444 , F v  (s) is set to  F   v  (w), i.e., a component of the short visual feature vector is set to a component of the longer visual feature vector, which results in sub sampling of the key intervals of  F   v . Step  1446  increments the variable w by m c . In test  1448 , it is checked whether w&gt;i W, i.e., if a complete key interval has been sub sampled. If not  1450 , the variable s is incremented in by 1 in step  1452  and step  1444  is repeated. If yes  1454 , in step  1456  i is incremented by 1 and w is set to i m+1. This w is the next component of  F   v  to sub sample. A further test  1458 , determines if s&gt;M. If no  1460 , the vector F v  (of length M) is not completely filled yet and s is set to s+1 in step  1452  and the process is repeated starting with step  1444 . If test  1458  is true, on the other hand, the vector F v  of fixed length M is output in step  1464  and the sub sampling process stops in  1466 . 
   The process  1470  starts at step  1471 . In step  1472 , the variables W (the number of regions per key interval), N (length of F v ), M (length of F v ), m f =└N/M┘ (the averaging rate), m=M/W, and the vector  F   v  are input to the system. Additionally, the variables i, w, s are set to one, i=w=s=1. In step  1444 , F v  (s) is set to the average of the m f  component of  F   v ,  F   v  (w),  F   v (w+W),  F   v  (w+2W), . . . ,  F   v (w+m f −1)W), i.e., a component of the short visual feature vector is set to the average of corresponding m f  component of the longer visual feature vector  F   v . That is,
 
 F   v ( s )=[   F     v ( w )+   F     v ( w+W )+   F     v ( w+ 2 W )+ . . . +   F     v ( w+ ( m   f −1) W )]/ m   f .
 
   Step  1476  increments the variable w by m f . In test  1478 , it is checked whether w&gt;i W, i.e., if a complete key interval has been handled. If not  1480 , the variable s is incremented in by 1 in  1482  and the averaging step  1474  is repeated. If yes  1484 , in step  1486  i is incremented by 1 and w is set to i m+1. This w is the right component of  F   v  to proceed with further filling of F v  by averaging. A further test  1488 , determines if s&gt;M. If no  1460 , the vector F v  (of length M) is not completely filled yet and s is set to s+1 in step  1482  and the process is repeated starting with step  1474 . If test  1488  is true, on the other hand, the vector F v  of fixed length M is output in step  1494  and the averaging process stops in  1496 . 
     FIG. 15  shows a flowchart of the process  1500  of combining the visual feature vector F v    1510  and the textual feature vector F t    1520 . The output is a combined feature vector F  1560  that represents such disparate sources of information as textual information (e.g., speech transcript) and visual information (i.e., images or sequences of images). These vectors are used in process  400  of  FIG. 4  to induce classifiers. The vectors may be labeled or unlabeled and the learning is supervised and unsupervised, respectively. Process  1500  (in  FIG. 15 ) are optionally transformed to assure compatibility in various ways. There is an optional transformation  1530  of the visual feature vector F v  and an optional transformation  1540  of the textual feature vector F t . These transformations can be with respect to range of the feature values appearing in the two kinds of vectors F v  and F t , this is achieved by scaling (normalization) or re-quantization. The transformations may also have to be performed with respect to the competitive lengths of the two kinds of vectors with respect to some norm or measure. In step  1550  the textual feature vector F t    1520  and the visual feature vector F v    1510  are combined by concatenation to produce a unified representation in a single vector of disparate kinds of features. Having created the unified representation, the combined feature vector F  1560 , of the training data, standard methods of classifier induction are used. 
   Turning our attention now to  FIG. 16 , here is shown a system diagram of the present invention. The media item  1601  could be in any format like NTSC/PAL  1603 , MPEG1/MPEG2 file or stream  1609 , AVI file or stream  1613 , some arbitrary format xxx  1619  or Real Video  1623 . Depending on the format of the media, it is processed through the corresponding decoder, NTSC/PAL  1603  through the frame grabber hardware  1607 , MPEG1/MPEG2 through the MPEG Decoder  1611 , AVI  1613  through the AVI Decoder  1617 , some unknown format xxx  1619  through the corresponding decoder  1621 , and Real Video  1623  through the Real Video Decoder  1627 . 
   The output of each of the decoders in  FIG. 16  will be some generic video frame format like RGB or YIQ  1629  and the data will be in machine readable form. The media item categorization algorithm operates on this  1629  data. The categorization engine  1633 , first computed visual and textual vector representations, F v  and F t , respectively from the decoded media item  1629  and uses category representations  1631  to generate the category  1637 . 
   In  FIG. 17  the idea of associating a feature vector F with a blocks of the media item as opposed to associating the feature vector with the entire media item is introduced. The feature vector F is then a function of time of frame number F(t) or F(n), as introduced in  FIG. 17B . To emphasize the fact that the media item or stream has time associated with it, we refer to such a media item as M(t)  1750 . But first consider  FIG. 17A  where is show the feature vector F  1755  computation of a media item  1700 . Let the media item  1700  be a visual media stream. The stream contains textual information  1710 , either in the form of captioning or in the form of an an audio track (or both), and visual information  1720  in the form of a sequence of images (frames). An individual frame in the media stream is associated with a frame number n  1725 , which corresponds to unique time t  1715  in the stream, given that the stream starts at n=0 and t=0. The media item is of length T  1718  in terms of time and of length N  1728  in terms of frames. If there are 30 frames per second and T is expressed in seconds, then N=30 T. The textual feature vector F t  (refer to  FIG. 14 ) is computed from the textual information  1710 . The visual feature vector F v  (refer to FIG.  14 ) is computed from a subset of the frames, as described in  FIGS. 7A and 7B . In the case of  FIG. 17A , these are the key frames (or key intervals) n 1  ( 1730 ), n 2  ( 1735 ), n 3  ( 1740 ), and n 4  ( 1745 ). 
     FIG. 17B  is focussed on generating feature vectors F(t) or F(n) with the ultimate aim of dividing up a media steam  1750  into contiguous segments of one or more categories. In  FIG. 17B , a media stream  1750  of length T  1754  seconds and N  1758  frames is shown. As opposed to the media item  1700  in  FIG. 17A , where it is assumed that the item is about one subject or category, for media item  1750 , the topic, subject or category of the media item may change as a function of time t  1715 . Similar to  FIG. 17A , key frames (or key intervals) n 1  ( 1761 ), n 2  ( 1762 ), n 3  ( 1763 ), n 4  ( 1764 ) through n 9  ( 1769 ) are selected based on some visual criterion. However, in this case, the textual F t  and visual feature vector F v  are determined from an continuous subset of the media stream, subset  1780  of the textual information and subset  1790  of the visual information. This continuous subset of textual and visual information is called a “block.” Here the continuous subset is of length T 0    1785  seconds or N 0    1795  frames (N 0 =30×T 0 ). The textual feature vector F t  is computed from all the textual information in block  1780 . The visual feature vector F v , on the other hand, is computed from the key intervals that are contained in block  1790 . For the block, the key intervals are n 2  ( 1762 ) and n 3  ( 1763 ). A visual feature vector F v  is computed from these key intervals by one of the methods described in  FIG. 13 . The textual F t  and visual feature vector F v  are then combined into a overall feature vector F(n 0 ) or F(t 0 )  1775  by the process described in  FIG. 15 . By using such a moving block of both visual and textual information, a category and/or topic C(n) or C(t)  1780  is obtained as a function of time t  1715  or frame number n  1725 . It is this function that this invention uses to divide the media item into segments that correspond to different categories. 
   In  FIG. 17C , the media stream  1750  of length T  1754  seconds and N  1758  frames is indexed in a third way, by word count w  1797 . Here there are W  1799  words in media item w, 1≦w≦W. Category and or topic can then also expressed a function of word count, w, i.e., C(w)  1785 . 
   Continuing to  FIG. 18 , here is shown the process  1800  for dividing up the media item M(t)  1750  into contiguous time segments where each segment is associated with one or more classes and the segments are optionally aggregated into larger more homogeneous segments according to certain rules. Here the rules can be heuristic rules, learned rules from training data, or both. 
   Storage device  1810  contains one or streaming multimedia items M(t)  1750 . Output of this device  1810  is the temporal multimedia item  1750 , which, importantly, in this figure is not of one single category but can be a concatenation of one or more multimedia segments of different category (subject/topic). Device  1810  streams a multimedia item M(t)  1750 , which in block process  1820  is divided up into blocks. This is the process described in  FIG. 17 . The output  1820  is a block of multimedia data B(t)  1830 , which is a function of time t. Each block contains a portion of textual data  1780  (see  FIG. 17 ) plus a portion of visual data  1790  ( FIG. 17 ). The length of these blocks depends on the frequency of change in category in the multimedia item M(t)  1750 . Each block B(t)  1830  is in itself a multimedia item. Using the prior art described in  FIG. 1 , a sparse textual feature vector F t (t) is extracted, further, using the novel techniques described in  FIG. 13 , a sparse visual feature vector F v (t) is extracted in step  1840  from block B(t)  1830 . Using the vector combining process described in  FIG. 15 , these two vectors are combined in process  1840  into vector F(t)  1850 , which is the output of process  1840 . The application process  1860 , uses the classification phase  615  (described in  FIG. 6  with the target media stream M  660  equal to B(t)  1830 ). Output  1870 , C(t), of this application process  1860  is a categorization/classification of the media stream M(t)  1750  as a function of time t. At each time t, one or more categories are associated with media stream  1750 , denoted as C(t). 
   There are a number of problems associated with this output  1870  C(t). Even if the classification error for each block is small, say 5%, for example, there is a possible random error in every 20 key frames, key intervals, or blocks of key intervals. These problems are described in the next figure,  FIG. 19 . An optional aggregation process  1880  develops the output  1870 , C(t), into output  1890 ,  C (t), according to one or more aggregation rules. These rules can be heuristic and/or can be learned. A heuristic approach is given in  FIG. 20 . An approach, based on machine learning, is described in  FIG. 21-22 . 
   But first, in  FIG. 19 , the problems with output  1870  C(t) are described.  FIG. 19A  shows a graph  1900  of the discrete changes in the true category C t    1905  of a streaming media item M(t)  1750 , as a function of time t, frame number n, or word count w  1906 . The length of the media item is T seconds, N frames, W words  1903 . This media item M(t) is, for example, a portion of a news program. The media item starts with a segment  1910  about “US President,” category c 1 , this is followed by a segment  1915  in the category “European Union,” category c 2 . Segment  1915  is followed by a segment  1920  about the weather  1920  (category c 3 ), which is also the category of the sixth segment  1935 . Between the two weather segments  1920  and  1935 , there is a segment  1925  about “Free trade,” category c 4 , followed by a segment about “Crime in the cities,” category c 5 . The media item ends with segment  1940  about, “Baseball league,” category c 6 . Hence, the true category C t    1905  of the media item is a function of time, for example, for interval  1910  C t (t)=c 1 , for interval  1915  C t (t)=c 2 , for intervals  1920  and  1935  C t (t)=c 3  (the weather), and so on. The function C t (t) is a discrete function, the function can take on one or more values of a finite number of categories. (A media item does not have to be classified into one distinct class, it may be classified into a multitude of classes.) 
     FIG. 19B  shows a graph  1950  of the estimated categorization function (classifier output) C(t)  1955  ( 1870  in  FIG. 18 ) as a function of time t  1906  as obtained by application process  1860  in  FIG. 18 . This function is an illustrative example and not an actual depiction of what the graph would look like. In reality the function is discreet and misclassifications do not necessarily lie close to the true class. The function is also a function of frame number n  1906  and word count w  1906 , C(n) and C(w), respectively. Category C(t)  1955  is shown as a one-dimensional function  1955 . If the category C(t) could be computed perfectly, this function would be equal to the discrete function C t (t) of  FIG. 19A . However, due to noise and other problems, the function C(t)  1955  is only approximately equal to the true function C t (t). That is, C(t)≈c 1    1960  in segment  1910 , C(t)≈c 2    1965  in segment  1915 , C(t)≈c 3    1970  in segment  1920 , . . . , C(t)≈c 6    1990  in segment  1940 . Here “C(t)≈c 1 ” means that C(t)=c 1  at many of the sample points t where C(t) is computed. 
   A first problem is that the calculated categorization of the blocks within the true segments,  1910 ,  1915 ,  1920 , . . . ,  1940 , varies and is subject to outlying categorizations within the true segments from the true category C t    1905 , that is, C(t) not always equals C t . Furthermore, because of the block process (described in  FIG. 17 ), there will always be blocks that span different segments of different categories. This will be the case at the true segment boundaries like  1991 ,  1994 ,  1996 , where the category changes. Consider, for example, the boundaries between true category segments  1910  and  1915 . Here a block of data  1961  is used denoted by B(t)  1962 , from this block of data, a feature vector F(t)  1963  is computed. This feature vector is classified into a class C(t)  1964  according to media item categorization described in, e.g.,  FIG. 16 . However, because the block  1961  spans multiple true category segments, the output of the classifier in these type of regions is unreliable. 
     FIG. 20A  shows a temporal display of a portion of the media stream of  FIG. 19 . This partial media item starts with a segment  1910  of category c 1  “US President,” this is followed by a segment  1915  of category c 2 , “European Union .” Segment  1915  is followed by a segment  1920  about the weather  1920  (category c 3 ). The true C t (t)  1905 , as a function of time t, frame number n, or word count w  1906  is shown in the upper region. Also shown is the classification of the media item as a function of time C(t)  1955  in the lower region. In reality this classification is a sequence of categories, c 1 c 8 c 1  . . . c 2 c 1 , each classification is obtained from a block of multimedia B(t)  1962  where the blocks are determined as in  FIG. 17  and the category for each block is determined as in described in  FIG. 18 . Within a media segment of a certain category, there will be random misclassifications. This is shown, for example, as the C(t)  1955  for segment  1910 , which is c 1 c 8 c 1 c 1 c 2 c 1 c 1 c 1 c 7 c 1 c 1 . A number of heuristic rules  2050  on how the aggregate the local classifications are shown in  FIG. 20A . A first rule is that a media item segment of a certain category should be at least s seconds  2055 , where s is dependent on the type of programming. An immediate consequence of this rule is that there are few changes in category over time, that is, C(t)  1955  is a discrete function and each discrete value is taken on for longer time intervals (&gt;s seconds  2055 ). An aggregation process  1880  ( FIG. 18 ) process groups the local classifications c into larger segments of the same category. Examples of such rules are 
   
     
       
         
             
             
             
           
             
                 
                 
             
           
          
             
                 
               c x c y c x  → c x c x c x   
               2060 
             
             
                 
               c x c x c y c y c x  → c x c x c x c x c x   
               2065 
             
             
                 
               c x c y c y c x c x  → c x c x c x c x c x   
               2070 
             
             
                 
               c x c x c y c y c y c x c x  → c x c x c x c x c x c x c x   
               2075 
             
             
                 
                 
             
          
         
       
     
   
   Repeated application of these rules changes the local classifications which may initially be fragmented, and will change the local classifications into more contiguous segments of constant category.  FIG. 20A  concentrates on rules of sequences of block classifications, or block classifications within segments of a single category. That is, these are region-based rules. 
     FIG. 20B , on the other hand, concentrates on rules for finding the boundaries between segments of different categories. Classification C(t)  1955  of the blocks around the boundaries are unreliable because these classifications are based on blocks B(t)  1830  that span multiple categories of the underlying media M(t)  1750 . A number of cues  2000  from the multimedia can be used to more accurately find category boundaries. The exact location of the categories boundaries can be pinpointed by using the various modalities that are present in a media item. The cues in these various modalities are audio silences  2005 , speaker changes  2010 , end-of-sentence indications in the speech transcript  2015 , a shot break in the visual track  2020 , and the presence of “&gt;&gt;” in the closed-captioning (“&gt;&gt;” is intended to indicate change of subject)  2025 . 
   Given the combined feature vectors F(t), i.e., the vector representing the visual information F v (t) combined with the vector representing the textual information F t (t), each block can be classified into a category. One way to achieve this is to use a classifier to categorize every block independently using the combined feature vector of the block. A series of heuristic rules such as described in  FIGS. 20A and 20B  can then be used to aggregate the categorization and more accurately determine the category boundaries. 
   Another way to achieve this is to build a model for predicting the categories of the blocks, consisting of states, based on states of other blocks. Each block can be associated with one state. The categorizer then predicts a cost of each state associated with every block based on the combined feature vector. The optimal sequence of states is selected by minimizing the cost. The category can be equal to the state or the category can be a function of the state. This approach is described in  FIGS. 21 and 22 . 
   Consider the simplified flowchart of  FIG. 21A  that takes media stream M(t)  2105  as input, or, equivalently, that takes a stream of visual and textual feature vectors F(t)  2110  as input. Here is shown system  2100 , a categorization process (also called, application process)  1860  followed by aggregation process  1880 , exactly as in  FIG. 18 . Assume we only have two categories, i.e., “sport” and “disaster” denoted by C 1  and C 2 , respectively. The features F i =F (t i )  2110  for each block can be one of three values: F s  sport scene, F t  talking head, and F d  disaster scene. The input  2110  to the system is a sequence of feature vector F i =F (t i )  2110 , which takes on values F s , F t  and F d  derived from (a block of the) media stream M(t)  2105 . 
   In a simple model, we let the state for each block be the category associated with the block, i.e., the category or state can only be “sport” or “disaster.” The classifier then is C (F s )=C 1 , C (F d )=C 2 , C (F t )=C 1  or C 2 . The output of the application process  1840  is C i =C(t i )=S(t i )  2115 . The idea then is to transform the sequence of ambiguous categories or states C i =C(t i )=S(t i ) into a sequence of unambiguous states  S (t i )  2120  (or, equivalently, a sequence of categories  C   i = C (t i )), which is of minimal cost, as computed in  1860 . That is, input media stream  2105  is categorized into a smoothly labeled (categorized) stream  2120 . 
   To achieve this, in the process  2150  of  FIG. 21B , using training data  2130  and an optional set of manual rules  2135 , a set of multi modality state transition costs L (C i , C j ); i=1, 2, j=1, 2 ( 2140 ) is determined. 
   A possibility for the process of determining state transition costs in process  2150  and the aggregation process  1880  is, e.g., a Markov model. In a Markov probability model, the probability of a sequence of states s 1 , . . . , s T  (each state takes on the value “sport”=C 1  or “disaster”=C 2 ) is decomposed as
 
 p ( s   1   , . . . , s   T )= p ( s   1 ) p ( s   2   |s   1 ) . . .  p ( s   T   |s   T−1 ).
 
   To estimate the conditional probability p(C 2 |C 1 ) (where C 1  or C 2  indicates “sport” or “disaster” and, e.g., p(C 2 |C 1 )=L(C 1 , C 2 ) in  2140 ), we count the number occurrences # (C 1 , C 2 ) of the sequence segments s i−1 , s i  in the training set  2130  such that s i−1 =C 1  and s i =C 2 . The conditional probability can then be estimated as
 
 p ( C   2   |C   1 )=#( C   1   , C   2 )/#{( C   1   , C );  C =sport, disaster}.
 
   That is, in system  2150  of  FIG. 20B , p(C 2 |C 1 ) is the number of times that category C 1  is followed by category C 2  divided by the number of times that category C 1  is followed by any category. In one model, the cost function L(C 1 , C 2 )  2140  an be selected to be −log p(C 2 |C 1 ). Hence, when the probability of the transition of one state to another is low, the corresponding cost is high. 
   In aggregation process  1880  (of  FIGS. 18 and 21A ) the most likely sequence of states s 1 , . . . , s T  is computed. The more likely a sequence of states s 1 , . . . , s T  is, the less the total cost
 
Total cost= L ( s   2   , s   1 )+ L ( s   3   , s   2 )+ . . . + L ( s   T   , s   T−1 )= L ( C   2   , C   1 )+ L ( C   3   , C   2 )+ . . . + L (C T   , C   T−1 ).
 
   This cost is minimized in  1880  over all possible sequences of states s 1 , . . . , s T , or, equivalently, over all possible sequences of categories C 1 , . . . , C T , resulting in  C   i = C (t i )= S (t i )  2120   
   For some example rules and costs now see  FIG. 22 . Assume that the block based classifier output indicates that
         F s →sport   F t →sport or disaster   F d →disaster
 
where F s , F t , and F d  are combined textual and visual feature vectors as shown in  2205 . Further assume that from the Markov state model  2150  described in  FIG. 21B , it is observed that  2210 :
       

   
     
       
         
             
             
             
             
           
             
                 
                 
             
           
          
             
                 
               L(sport, sport) 
               is 
               small 
             
             
                 
               L(sport, disaster) 
               is 
               large 
             
             
                 
               L(disaster, sport) 
               is 
               large 
             
             
                 
               L(disaster, disaster) 
               is 
               small 
             
             
                 
                 
             
          
         
       
     
   
   Imagine we have the sequence  2215 . Based upon the visual and auditory information, frame  2220  has feature vector F s  ( 2225 ), frame  2230  has feature vector F t  ( 2235 ), and frame  2240  has feature vector F s  ( 2245 ). That is we have the following the following sequence of feature vectors
         F s →F t →F s          

   Without state modeling, there are two interpretations from the classifier using  2205 :
         sport→sport→sport→sport   sport→disaster→sport→sport       

   The first interpretation is more likely  2250  by using our state model  2210 . 
   For the second sequence  2255 , we have frame  2260  has feature vector F s  ( 2265 ), frame  2270  has feature vector F t  ( 2275 ), and frame  2280  has feature vector F s  ( 2285 ). And we have the sequence of feature vectors
         F s →F d →F t          

   Again, there are two interpretations using the classifier (without state modeling, using  2205 ):
         sport→disaster→sport   sport→disaster→disaster       

   Consequently, using  2210 , the second interpretation  2290  is the more likely one.  FIGS. 21 and 22  are just an example of the use of state transition learning for smoothing the categorization of video over time and thereby arriving and a better segmentation. Other methods are obvious to those skilled in the art. 
   We continue by listing a number of business applications that are hard or impossible to achieve with prior art technology. After describing these applications, we pictorially give a further explanation in  FIGS. 23–27 . A first number of uses are:
         i. Categorizing media elements into a number of categories. For example, let the media element be a news item and let us predefine categories like, sports, weather, politics. The addition of visual features, beyond textual features, in the feature vector that represents the media elements allows for more precise (in the sense of a lower error rate) categorization of the media element.   ii. Automatically determining a number of natural categories that a set of media elements can be partitioned into. That is, discovering a set of categories that describes the set of media element compactly. Here the addition of visual features, beyond textual features, in the feature vector that represent the media elements allows for more refined partitioning of the media elements.   iii. Given that a media element has been classified into a category that is either predefined as in 1) or automatically learned as in 2) the current invention allows to more precisely assign a topic to the classified media element. Say that a media element has been classified as a sports video segment, the addition of visual features allows for better, (i.e., more accurately) assigning a topic (basketball, soccer, golf, etc.) to the given media element.   iv. The combination of textual features and visual features into one feature vector that represents the media element enables better detection or identification of a media element in a certain category with a certain topic, in a collection of media elements. Here, better detection means lower false positive and lower false negative rates.   v. The combination of textual features and visual features into one feature vector that represents the media element enables better detection or identification of a particular media element, in a collection of media elements. Here, better detection means lower false positive and lower false negative rates.   vi. The automatic generation of MPEG-7 descriptors, as defined by the International Organisation for Standardisation/Organisation Internationale de Normalisation, ISO/IEC JTC1/SC29/WG11 specification “Coding of Moving Pictures and Audio.” These descriptors are metadata items (digitally encoded annotations) which would be embedded in the bitstreams of videos (television; movies), sometime between the time of content creation (“filming” or “capture”) and the time of broadcast/release. These metadata items are then available to all downstream processes (post-production/editing stages of preparation of the complete video product, distribution channels for movie releases, or by receivers/viewers of the broadcast), for various purposes, in particular, retrieval from video archives by content-based querying (in other words, facilitating the finding of video clips of interest, or a specific video clip, from within large collections of video). The descriptors can be used to explicitly label events of interest in a video when they happen, such as the scoring of goals in soccer matches. Manually-controlled processes for creation of such annotations are available now, but the work is tedious and expensive.       

   The business process patent covers the use of the invention in each of the following specific business applications. 
   The invention has many direct applications: 
   A first application is locating (illegal) copies of media items on the Internet or other (public) databases. This application involves searching for digital copies of media elements on the Internet or other (public) databases. With the wide spread use of digital media (audio and video), the illegal copying and distribution of media are becoming a significant problem for the media industry. For example, there are a number of web sites that post illegal copies of video on the Internet. The media is encoded in one of the popular formats (AVI, MPEG1 or MPEG2 for video). Typically, the filenames under which the media is posted, are not indicative of the content of the media files. To identify a posted media item as a known media item, a comparison of the media content of the file (video and audio) is necessary. 
   The classification method described in this invention can be used to perform this comparison. In this case, the media items of interest (say several movies) are used as the reference media items to generate a representation in terms of textual/visual feature vectors. The classification engine is now deployed with these vectors. The target media items are transferred from web sites at the Internet to the computing system that houses the classification engine described in this invention. That is, the media element needs to be downloaded to the machine on which the classification engine is running. The downloading operation can be achieved in multiple ways, an operator could feed URL&#39;s to down loader software, which would download the files to the local machine or alternatively, a web crawler robot could be designed to locate URL&#39;s that hold media files. This can be done by looking at the filename extensions (.mpeg, etc). The URL&#39;s located by the crawler robot or human operator can be filtered based on various criteria, like size of the media items, to generate a list of URL&#39;s for downloaded software. 
   Once a target media item has been downloaded to the local machine, the classification engine is deployed to generate an report about similarities to the media items of interest. 
   This application provides functionality similar to video water marking in that the search engine detects the intrinsic properties (features) of the media item instead of the embedded water marks. 
   The present invention can be employed in the management of large video databases. Such collections of video clips (media items) need to be managed and searched in several environments like TV news, documentary, movie and sitcom productions. In these production environments, media items in the database will be used to produce program material, often the same media item in different productions. It is important to keep track of the usage of a media item from the perspective of rights management and royalty payments. The media item classification technologies discussed in this invention can be used in this process. 
   Every media item (s) which is entered into the database is first used as target media item and searched against a data structure of feature vectors that represent the reference media items in the database. This operation generates an index report of similar media items in the database. The media item to be entered into the database is stored along with similar media items, items of the same category. 
   The feature vector the data structure is stored along with the database and used retrieve content. As per the above procedure, the data structure of feature vectors will continually grow as more and more media items are added to the database. Several tasks, like removing redundant copies of the media items, selecting all media items in certain categories and with certain topics, etc., are straightforwardly accomplished. 
   This invention can be used to segment a video stream into a series of time-continuous media items. Given that a large number of (categories, topic) pairs are defined, a target video stream can be categorized, with a topic associated, as a function of t, time, or n, frame number. This is achieved by determining a combined textual/visual feature vector as a function of time or frame number. A certain window of text and visual information is used to compute the feature vector. Typically, this window is continuous time for audio data and, thus, continuous time for textual data. The window is discrete time for visual information, with the finest time resolution each frame. The window can be causal, i.e., [t, t+T] and [n, n+N], or non-causal, i.e., [t−T/2, t+T/2] and [n−N, n+M]. Here, (t, n), (t+T, n+N), (t−T/2, n−N), (t+T/2, n+M) are corresponding (time, frame number) pairs. The sequences of frames n−N, . . . , n and n−N, . . . , n+M, do not have to be subsequent frames or evenly spaced frames. The frames, n, n−N, n+M can be selected to be key frames, or frames with other special characteristics, e.g., maximal/minimal apparent motion or optical flow. 
   Classifying the media stream within the window, with the means described in the current invention, results in a discrete function C(t), i.e., the function can take on a potentially very large number of discrete values. This function will be roughly constant when the category, topic does not change and change to another constant value when the category, topic changes at some time t. This change will, in general, be gradual within the interval of the window. 
   Segmenting the media stream into time-continuous media items with distinct category, topic is now an issue of detecting changes in the value of C(t), as described in this patent application. There are a multitude of applications of this invention when the target media stream is segmented into separate media item. An application, for instance, is monitoring a given television for the occurrence of instances of a pre-specified set of media items. Such broadcast monitoring can be used to detect any type of pre-produced media material. The more typical use is for verifying the broadcasting of TV commercial messages (advertisements). Advertisers (companies whose products are being advertised) require an independent verification of the actual broadcasting of the commercial in order to make payments to the broadcaster. This process currently relies on a human viewer sampling the channel to verify the airing of a commercial. Hence, it is a labor intensive and error prone process. 
   The media element similarity measurement process described in this invention that combines textual and visual features can be used to serve the function of the human viewer. The commercial messages to be monitored is a set reference media items S. As described in this invention, these reference media element are used to generate a feature vector. To monitor a given channel, Channel X, (a target media stream) for commercials, a computing system that houses the classifier described in this invention is used. Depending on the type of broadcast (National Television System Committee (NTSC), Phase Alternating Line (PAL), digital, analog/digital audio), the media element (tuned to Channel X), visual, speech and captioning, is decoded and input to the computing system. 
   The media element classifier operates on the target media stream and produces a report. This report, in the case of commercial monitoring, will include the title of the commercial detected (reference media item identifier), the date and approximate time at which the commercial started, the date and approximate time at which the commercial ended and some type of classification quality, e.g., some similarity measure between reference media item and a segment of the target media stream, hence, a similarity measure between the combined feature vector of the target media segment and the combined feature vector of the reference media item. 
   An application of the present invention targeted towards the task of video indexing is video event detection. Video indexing can be defined as the operation of designating video items (media items) with certain predefined labels. There exists a significant body of prior art on the subject of video indexing. For example, consider a video of a soccer game, indexing this video will result in annotation table that looks as follows: 
   
     
       
         
             
             
             
             
           
             
                 
             
             
               Event Number 
               ~ Begin Time 
               ~ End Time 
               Label 
             
             
                 
             
           
          
             
               1 
               00:00:10:12 
               00:00:12:10 
               Penalty Kick 
             
             
               2 
               00:20:12:10 
               00:20:13:10 
               Field Goal 
             
             
               3 
               00:33:12:09 
               00:35:12:10 
               Penalty Corner 
             
             
               4 
               . . . 
               . . .  
               . . . 
             
             
                 
             
          
         
       
     
   
   There are several approaches to generating such reports, using software algorithms, described in the prior art. One of the approaches to event detection has been disclosed in R. Mohan. This approach uses reference video segments (examples of how a typical event would look like) and compares the target stream to the reference video segment based on generating codes for both the reference segment and the target segment. The discussion provided by Mohan however does not address the problem of performing such similarity measurements between a target stream and a multiplicity (large number) of reference streams, nor does it address including the textual information. Essentially, the target stream is simultaneously compared to the reference segments in a sequential fashion, one reference segment at a time, only using visual information. This inherently limits the number of reference segments that can be used in the comparisons. 
   The classification methods discussed in this invention can be applied to the video event detection problem as follows. The multiple example videos (media items) for the events to be detected are selected. These videos form the reference media streams S. The reference media streams are used to compute reference feature vectors. 
   The search engine described in this invention is deployed using these reference feature vectors. The target media stream (the video to be annotated) is fed to the appropriate decoder and the classification engine operates on the target media stream to generate the report. This report is a tabulation of the events in the target stream as shown in the table above. 
   This event detection is not limited to off-line video annotation, but also can be performed in real-time. Applications are in the arena of monitoring and human machine interaction. Events, such as, dangerous situations, human gestures combined with spoken command, etc. Can be detected in real time by employing the classification engine described in this invention with an appropriate feature vectors. 
   Another use of this invention is the categorization of multimedia email. Today, the content of email consists of text, possibly with attachments, or it consists of an html—hypertext markup language—file, which is itself text, possibly with references to other files or data objects that may provide non-textual data to be used by a browser when the html file is displayed. In the future, we envision email whose content is primarily a video message, possibly embedded in or accompanying a text file (e.g., an html file) used to control the display of the video message. 
   Such video email may well be created in a scenario such as the following, in which we assume the computer belonging to the user—here being Sam Sender—is equipped with speakers, a video camera trained on the user, and voice recognition software. Sam Sender wishes to send a message to Richard Receiver, a customer service representative at the Message Receiver Corp. Assuming that Sam Sender has named his computer Sybil, Sam says or signals, “Sybil, send a message to Richard Receiver at MessageReceiverCorp.com, with caption: ‘Complaint about billing error.’” The computer identifies the email address of the recipient, detects that the intended text caption for the message is “Complaint about billing error,” prepares to store the video and sound components of the message in a file, turns on the camera, and then says to Sam “Ready to record message.” The Sam recites his message using colorful language, while gesticulating and making faces. All of this is recorded by the computer. Sam finishes by saying or signaling “Sybil, send message.” The computer would then create a file containing the caption to be displayed, the video (including sound) that was recorded, the address of the sender, and any other information needed to enable the ultimate display of the captioned video by Richard Receiver&#39;s computer. 
   The classifiers induced by the method of this invention, if trained with appropriate training data, could be used to classify such video email. 
   For instance, in the scenario above, the mail server at the Message Receiver Corp. might apply a classifier to categorize the message as one that should be handled by Bill Bungler in the billing department, consequently sending a copy of the message directly to Bill Bungler in order to expedite resolution of the problem, while also sending a note to Richard Receiver informing him of this. 
   The following figures give a further depiction and description of the above described uses of the invention. They are provided as explanation of the business applications of the invention. 
     FIG. 23  is a business method for determining profiles of those multimedia item categories of particular interest to various different users retrieving multimedia content from private and/or public networks. Here a private and/or public network  2300 , contains multimedia items in multiple ways. The multimedia items URL 1 ( 2303 ), . . . , URL 2 ( 2306 ), . . . , URL N ( 2309 ) each could a be a Web page that is indexed by a Universal Resource Locator (URL). The multimedia items can also be databases containing multimedia content, i.e., Database 1 ( 2304 ), . . . , Database M ( 2308 ). From Web pages and these databases, the multimedia may be retrieved and viewed by the users through any means (e.g., FTP—file transfer protocol). The users,  2341 ,  2343 ,  2345 , . . . , i.e., , User i, i=1, . . . , m, retrieving multimedia documents  2303 ,  2306 , . . . and  2304 ,  2308  . . . , do so through the clients  2342 ,  2344 ,  2346 , . . . . These clients retrieve the multimedia items from one or more servers (not shown) at which the multimedia items reside. The clients can be thin with little compute power and storage available, but, in general, can be a computing device of any size. The clients  2342 ,  2344 , . . . , and  2346  are connected to the servers through communication links  2353 ,  2356 , . . . , and  2359 , respectively. These links can be wireless (e.g., wireless Ethernet, BlueTooth) or hardwired. 
   The communication links  2353 ,  2356 , . . . , and  2359  can be either overtly or covertly monitored, by processes which are indicated with the circular shapes,  2363 ,  2366  through  2369 . As part of these processes, the intercepted multimedia items can be categorized in terms of category, subject, topic, object, etc. For each individual user, a profile, profiles  2372 ,  2376 , . . . ,  2379 , i.e., Profiles j, j=1, . . . , m, are generated by building an textual-visual feature vector F from the intercepted multimedia. The profiles can be simply a set of such vectors, F i , i=0, . . . , n. These vectors can also be used to learn the preferences and dislikes of a user, users  2341 ,  2343 ,  2345 . Such a description of the user is stored in profiles  2373 ,  2376 ,  2379 , . . . . Applications of such profiles are direct marketing, user-profile-defined push news, news alerts. For instance, a cable company could learn the user profiles and recommend programs based on the user profile to individual viewers. Similar function can be built into set top boxes. 
     FIG. 24A  depicts a business application of the multimedia item categorizer subject of this invention for building index tables to organize a multimedia item database in terms of categories or indices. The database  2400  contains multimedia items, m=1, . . . , M,  2401 ,  2402 ,  2403 , . . . ,  2406 , . . . ,  2409 . Each multimedia item is of unknown category, subject, object or topic. There may also be global parametric data associated with these multimedia items. Process  2415 , is the series of computational step to determine a visual and textual feature vector, F=F v +F t    2416  for all multimedia items. These feature vectors are input to categorizer  2420  (a categorizer as described in  FIGS. 6A and 6B ). 
   For each multimedia item m ( 2406 ) in database  2400  a pair (item i, {cat j, cat k, cat l, . . . }) as  2425  is determined. These pairs  2422  are indicated by arrows  2427 , where the examples are (item n, {cat  2 }), (item i, {cat j})  2426 , (item m, {cat  3 ,  6 })  2429  and (item  3 , {cat N})  2428 . Each of these pairs contains an item, indicated by item i in  2425 , and a set of categories indicated by the set {cat j, cat k, cat l, . . . }. Hence, every item in the multimedia database is annotated by a series, or set, of categories that the particular item belongs to. These pairs (item i, {cat j, cat k, cat l, . . . }) are used to populate the hash table  2410 . 
   This table  2410 , has a number of N indices  2412  and an associated number of N entries  2414 . The indices  2412 , cat  1 , . . . , cat i, . . . , cat N are associated with the N possible categories that are (expected to be) found in the database. Here these categories can be learned or can be a predetermined set of categories. The entries  2414 , e.g.,  2415 ,  2416 ,  2417 ,  2418 ,  2419 , contain the item numbers that are associated with the corresponding categories in the indices  2412  as is determined by categorizer  2420 . Hence,  2427  (item n, {cat  2 }) is placed in index Cat  2  with corresponding entry  2416  containing the item number n. Note that each entry can contain multiple item numbers, indicated by entry  2416  {j . . . } This simply means that all these items {j . . . } are categorized as the corresponding category number in the index  2412  associated with entry  2416 . Of course, an item can also be classified into multiple categories, indicated by arrows  2428  {item m, {cat x, y}) and  2429  {item m, {cat x, y}). Table  2410  can now be used to retrieve multimedia items of a certain category. For example, to find items of category i the corresponding item numbers is given in list or set {j . . . } as in  2417 . That is, the set of multimedia items contains item j. The next figure further explains this process. 
     FIG. 24B  is a business application for retrieving of multimedia items from database  2400  based on the database organization and database indexing described in  FIG. 24A . Here is again shown the multimedia database  2400  containing multimedia items, m=1, . . . , M,  2401 ,  1402 ,  2403 , . . . ,  2406 , . . . ,  2409 . The populated hash table  2410  from  FIG. 24B , contains (index, entry) pairs such as  2429  {item m, {cat x, y})., indicating that item m and 6 belongs to categories x and y. Hence, the search for multimedia items of (say) category Cat i, simply is performed by indexing into index entry pair, (Cat i, {j . . . })  2417  of table  2410 . The list {j . . . } or set {j . . . } in the corresponding entry of index  2417  are the multimedia items that are of category Cat i. The entries  2414  contain pointers  2460 ,  2462 ,  2464 ,  2466 , . . . ,  2468  to the multimedia items  2401 , . . . ,  2409  in database  2400 . 
     FIG. 25  is a business application and system  2500  where one or more of multimedia items are received and the items are routed according to the multimedia items category. The systems takes as input any multimedia item  2510 . This item may contain streaming multimedia and this could be a streaming multimedia item of different categories in different time segments of the stream. This multimedia item is input  2515  to the categorizer  2520 . An incarnation of categorizer  2520  is, for example, shown in  FIG. 18 . Switch  2530  is a router that can direct the multimedia item  2510  to various destinations, Destination 1 ( 2550 ), Destination 2 ( 2560 ), through  2570 , Destination N ( 2590 ). Switch  2530  is different from an ordinary switch in that the multimedia item  2510  can be routed to one or more destinations  2540  at the same time. An example in  FIG. 25  is given by extra switch position  2538  in addition to  2535 . Herewith, multimedia item  2510  is sent to Destination 2 ( 2560 ) but also to some other destination Destination n, with n determined by  2538 . 
   A use of system  2500  is, for example, the categorization of incoming multimedia e-mail and the determination what the correct procedure for handling each particular incoming e-mail is. The incoming e-mail can be routed to a sender, a folder, a person, a personal folder, a corporate folder, and a corporate department. The multimedia e-mail may also be duplicated before being routed. 
     FIG. 26  is a business application  2600  where one or more multimedia items  2610  are received and for each item some decision  2690  based on the category of the multimedia item is made. This system is broader than the system described in  FIG. 25 , in that the business decision  2690  does not need to be a decision to route the multimedia item to certain physical or logical places. The systems takes as input one or more multimedia items  2610 . These items may contain streaming multimedia and could be a streaming multimedia item of different categories in different time segments of the stream. This multimedia item is input  2615  to the categorizer  2560 . The categorizer may, for instance, be as the one shown in  FIG. 18 . The output  2625  of categorizer  2620  is input to the decision process  2690 . The multimedia items  2610  could be digital multimedia copies and the feature vectors computed by categorizer  2560  could represent restricted multimedia items and the comparison determines if the digital multimedia copies are similar to restricted multimedia items and the decision is that the digital multimedia copies are subject to a second restriction. This second restriction could include any one or more of the following: a copyright restriction, a trademark restriction, intellectual property restriction, parental guidance restriction, common decency restrictions, user-defined restrictions. 
     FIG. 27  is a business application  2700  where one or more multimedia items on a private and/or public network  2710  are examined and for each found multimedia item a decision based on the category of the multimedia item is made. The private and/or public network  2710  contains an, in principle, very large number multimedia items. Examples of these items in  FIG. 27  are the items labeled  2702 ,  2704  and  2706 . These items can be accessed and downloaded, if necessary, by process  2720 . Process  2720  is the combination of a crawler process  2730  and a down loader process  2740 . The crawler process  2730  is a process that finds the multimedia items  2702 ,  2704 , . . . ,  2706  on the private and/or public network  2710  The down loader process  2740 , on the other hand, examines the multimedia items  2702 ,  2704 , . . . ,  2706 , and if multimedia content is found that is suspicious according to a set of predefined rules which can contain a categorizer as  2780 . If the content is suspicious, the multimedia item is downloaded and is input  2775  to the categorizer  2780 . The categorizer may, for instance, be as the one shown in  FIG. 18 . The output  2785  of categorizer  2780  is input to the decision process  2790 . The multimedia items  2702 ,  2704 ,  2706  could be digital multimedia copies and the feature vectors computed by categorizer  2780  could represent restricted multimedia items and the comparison determines if the digital multimedia copies are similar to the restricted multimedia items and the decision is that the digital multimedia copies are subject to a second restriction. This second restriction includes any one or more of the following: a copyright restriction, a trademark restriction, intellectual property restriction, parental guidance restriction, common decency restrictions, user-defined restrictions.