Abstract:
Techniques are provided to classify patterns in isogenous pattern sources. Techniques are provided to determine a computationally inexpensive upperbound on the true score or joint probability of the field label and field features over all field labels. Candidate field labels associated with promising upperbound scores are dynamically queued. True scores are computed for a subset of the candidates fields resulting in reduced computations to determine a field label. Techniques are also provided to determine optimal variables for any system with shared constraints.

Description:
INCORPORATION BY REFERENCE 
   This Application incorporates by reference: entitled “DOCUMENT IMAGE DECODING USING AN INTEGRATED STOCHASTIC LANGUAGE MODEL” by A. Popak et al., filed May 12, 2000 as U.S. patent application Ser. No. 09/570,730; in its entirety. 
   BACKGROUND OF THE INVENTION 
   1. Field of Invention 
   This invention relates to techniques for style conscious field classification of isogenous patterns. 
   2. Description of Related Art 
   Conventional recognition systems have difficulty correctly classifying less than optimal patterns of text images. In attempts to improve classification using these conventional systems, some researchers have attempted to exploit style consistency information. Sarkar et al, in “Classification of Style Constrained Pattern Fields” in Proceedings of the Fifteenth ICPR, pp. 859–862, Barcelona 2000, IEEE Computer Society Press, and in “Style Consistency in Isogenous Patterns” in Proceedings of the Sixth ICDAR, pp. 1169–1174, Seattle, September 2001, each incorporated by reference in their entirety, discuss attempts to improve classification of patterns by determining the joint probabilities of the field label and field-features over all field labels. Due to dependencies among patterns in the field, these conventional systems require optimization of a field score over all possible field labels. However, the determination of joint probabilities of the field label and field-features over all field labels is computationally expensive. Moreover, the number of computations necessary to determine a field label increases exponentially with increasing field-length. This limits the application of these conventional systems for longer fields and larger texts. 
   SUMMARY OF THE INVENTION 
   The systems and methods according to this invention provide for style conscious field classification of isogenous or common origin patterns. The systems and methods according to this invention provide for style conscious field classification of isogenous image, audio and video patterns. Systems and methods according to this invention compute an upperbound value of the true score of a field label. Candidate field labels with promising upperbound values are dynamically queued. The systems and methods according to this invention determine an upper bound on the field-label conditional field-feature likelihood, for a subset of all possible field labels. The systems and methods according to this invention provide for optimization of any variables with shared or joined constraints. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is an overview showing an exemplary style conscious field classification system, according to this invention; 
       FIG. 2  is an exemplary pattern recognition flowchart showing pattern recognition based on an exemplary method of style conscious field classification according to this invention; 
       FIG. 3  is an expanded flowchart of an exemplary method of style conscious field classification according to this invention; 
       FIG. 4  is an exemplary style conscious field classification system according to this invention; 
       FIG. 5  shows an exemplary data structure for storing current label information according to this invention; 
       FIG. 6  shows exemplary successor labels according to this invention; 
       FIG. 7  shows an exemplary data structure for storing current-best label information according to this invention; 
       FIG. 8  shows an exemplary data structure for storing priority queue information according to this invention; 
       FIG. 9  shows an exemplary portion of a text pattern; 
       FIG. 10  shows an exemplary portion of an audio pattern; 
       FIG. 11  shows an exemplary portion of a video pattern; 
       FIG. 12  shows an exemplary data structure for storing patterns associated with a field; 
       FIG. 13  shows an exemplary data structure for storing factorizable upperbound contribution values associated with patterns in a field according to this invention. 
   

   DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     FIG. 1  is an overview showing an exemplary style conscious field classification system according to this invention. The style conscious field classification system  100 , as well as style conscious field classification based visual object recognition system  101 , style conscious field classification based audio recognition system  102  and style conscious field classification based OCR system  103  are connected via communications links  99  to web-enabled personal computer  400 , phone  500 , web-enabled portable device  600  and information repository  300 . The information repository  300  contains text image patterns  1000 , audio patterns  1001  and video patterns  1002 . 
   In a first exemplary embodiment according to this invention, a user of web-enabled personal computer  400  forwards a request for the optical character recognition of text image  1000  to the style conscious field classification based OCR system  103 . The text image patterns  1000  stored in information repository  300  may include PNG, TIFF, J-PEG, GIF, Adobe PDF image files and/or any known or later developed image format. 
   In response to the request, the style conscious field classification based OCR system  103  retrieves the requested text image patterns  1000  from information repository  300  via communications links  99 . The style conscious field classification based OCR system  103  recognizes text associated with the text image patterns. For example, the style conscious field classification based OCR system  103  exploits the fact that character bitmaps in word generally share the same font. Since patterns of a class are likely to be rendered less variedly by a single source than by multiple sources, this information can be used to improve the classification process. 
   Style consistency modeling can be divided into weak and strong style consistency. Weak style consistency includes indicating how “a” is written the same way each time by the same author. Strong style consistency determines the dependencies between the letters. For example, how “a” looks depends on how “b” looks. Strong style consistency is especially useful when the fields are not long enough for conventional classification systems to determine the parent style. The style conscious field classification based OCR system  103  uses these determined interdependencies to improve classification. After recognizing the text associated with the text patterns, the style conscious field classification system  103  forwards the recognized text to the personal computer  400  via communications links  99 . 
   In various other exemplary embodiments of this invention, style field classification based field recognition may be used in conjunction with optical character recognition systems without departing from the scope of this invention. For example, an optical character recognition product such as ScanSoft Corporation&#39;s TextBridge® product may be used to perform pre-processing operations such as layout analysis, segmentation and the like. Character recognition is then determined by the style conscious field classification based OCR system  103  as discussed above. 
   It will be apparent that in various other exemplary embodiments according to this invention, the style conscious classification system  100  may be located within web-enabled portable device  600  and/or form part of a handwriting recognition circuit and/or software routine within web-enabled portable device  600 . For example, a style conscious field classification based handwriting recognition system (not shown) may be included within web-enabled portable device  600  to improve the recognition of handwritten patterns. Text recognized from the handwritten patterns is then suitable for further processing such as editing and the like. In various other exemplary embodiments of this invention, a generalized style conscious field classification system  100  placed at any location accessible via communications links  99  is used to recognize patterns in various types of isogenous information. 
   In a third exemplary embodiment according to this invention, a user of phone  500  may initiate a speech based request for text image patterns  1000  stored in information repository  300 . The audio patterns of the speech based request are forwarded to the style conscious field classification based audio recognition system  102 . Sampled features in the signal frames of the speech segments are used to recognize phonemes and/or words. Since the audio patterns originate from the same source, the user of phone  500 , style consistency such as same-speaker intonation, accent and the like can be used to improve the accuracy of the recognition. 
   The recognized audio text corresponding to the speech request is then forwarded to the information repository  300 . The object of the voice or speech request, the text image patterns  1000 , are then forwarded to a display device associated with the user. It will be apparent that the display device may include a facsimile machine, a text to speech synthesizer or any known or later developed method of output or display. It should be noted that the style conscious field classification system  100  may also be incorporated directly within phone  500 , placed within information repository  300  or placed at any location accessible via communications links  99 . 
   In a fourth exemplary embodiment of this invention, a user of web-enabled personal computer  400  requests the recognition of visual objects in video patterns  1002  contained within information repository  300 . The request is forwarded to the style conscious field classification based visual object recognition system  101 . For example, a user of web-enabled personal computer  400  may request the recognition of a corporate logo, insignia or other visual object in the video patterns of a movie. The style conscious field classification based visual object recognition system  101  recognizes visual objects in the video patterns of the movie and attempts to determine all instances of the specific corporate logo or insignia. Various other embodiments of the systems and methods of this invention may be used to automatically determine objects and/or user actions or responses in a computer monitored or smart environment. For example, various user actions may be recognized and automated responses determined. 
   The style conscious field classification based visual object recognition system  101  exploits the style consistency in video patterns to improve visual object recognition despite camera angle changes, focus and other changes within the video patterns  1002 . In various other exemplary embodiments of this invention, indices into the video patterns may be returned indicating all video locations containing the recognized visual objects. 
   In various other exemplary embodiments of this invention directed to audio patterns processing, the style conscious field classification based audio recognition system  102  is used to filter telephone, television, radio and other audio patterns for keywords. For example, keyword audio monitoring can be used to automatically monitor press coverage of a company, a product or any topic of interest. 
     FIG. 2  is an exemplary pattern recognition flowchart showing pattern recognition based on an exemplary method of style conscious field classification according to this invention. The process starts at step S 100  and continues immediately to step S 110  where the source of patterns is selected. As discussed above, the patterns may be selected from any isogenous source. For example, the text image patterns output from scanning of textual information, audio telephone conversations, radio and television audio broadcasts and video patterns such as television broadcasts, movies in MPEG, motion-JPEG, real-video or any other known or later developed source of isogenous patterns. After the source of patterns has been selected, control continues to step S 120 . 
   In step S 120 , a first field within the isogenous patterns is selected. For example, if the isogenous patterns are text image patterns, the first field is likely to be a word composed of discrete characters. The field is selected and control continues to step S 130 . For speech based audio patterns, phonemes and/or speech utterances are determined. 
   The contribution of the candidate pattern labels to the factorizable upperbound score are determined for each position in the field in step S 130 . In various exemplary embodiments according to this invention, the contribution of each pattern label is determined and stored in an array in memory or any known or later developed method of storing information. After the contribution of each pattern label to the factorizable upperbound score is determined, control continues to step S 140 . 
   In step S 140  of an exemplary embodiment according to this invention, each candidate pattern label is re-labeled using the sorted index offsets as labels. However, it will be apparent that re-labeling of candidate fields is used merely to facilitate discussion of the various candidate field labels. Thus, in various other exemplary embodiments, the candidate field labels may be used directly without departing from the scope of this invention. Control then continues to step S 150 . 
   In step S 150 , a field label is determined using a style conscious field the exemplary method of style conscious field classification shown in the expanded flowchart of  FIG. 3 . After the field label is determined in step S 150 , control continues to step S 160 . 
   In step S 160 , a determination is made whether the last field in the pattern source has been reached. If the current field is not the last field, control continues to step S 170  where the next field in the pattern source is determined. After the next field in the pattern source is determined, control continues to step S 130  and steps S 130 –S 160  are repeated until the last field in the pattern source is determined. When it is determined in step S 160  that the current field is the last field in the pattern source, control continues to step S 180  and the process ends. 
   It will be apparent that the method for style conscious field classification according to this invention may output different types of recognized patterns based on the type of isogenous patterns serving as input and the types of features chosen to represent the recognized patterns without departing from the scope of this invention. 
     FIG. 3  is an expanded flowchart of an exemplary method of style conscious field classification according to this invention. Index labels used to label candidate fields are used here merely for discussion purposes. Any method of labeling candidate field labels may be used in the practice of this invention. The current label {1,1,1} indicates the current candidate label is associated with best candidate pattern labels in the first, second and third positions. Similarly a current label of {2,2,1} indicates second best candidate pattern label in the first position, second best candidate pattern label in the second position and the best candidate pattern label in the third position. The ordering of candidate pattern-labels is according to their contribution to an upperbound score as discussed later. As discussed above, the index representation of candidate field labels is used merely for discussion purposes and it will be apparent that any data structure and/or labeling of candidate field labels and any data structure useful for storing candidate field labels may be used in the practice of this invention. 
   The exemplary style conscious field classification starts at step S 200  and immediately continues to step S 210  where a current label, a current-best label and a priority queue are initialized. In one exemplary embodiment according to this invention, the current label and the current-best label are maintained as lists of elements initialized to the values {1,1,1}. The priority queue is maintained as a list of elements initialized to NIL or the empty list { }. In various other exemplary embodiments according to this invention, the list of elements in the current label, the current-best label and the priority queue may be stored in a heap data structure, an array in memory or any known or later developed method of storing the label and priority queue information. After initializing the current label, the current-best label and the priority queue information, control continues to step S 220 . 
   In step S 220 , the successor labels of the current label are determined. The successors of the current label are determined by increasing each successive index position by one, until a maximum number of classes C is reached. For example, assuming the current label is {1,2,1} and C=2, then successors of the current label {1,2,1} are {(1+1),2,1} and {1,2,(1+1)} which simplify to {2,2,1} and {1,2,2}. After the successor labels of the current label are determined, control continues to step S 230 . 
   The priority queue of labels is merged with the previously determined successors of the current label in step S 230 . For example, a factorizable upperbound score may be determined for each successor label entry to be added to the priority queue. The determination of the factorizable upperbound is discussed further below. Successor label entries are then merged into the priority queue based on the determined factorizable upperbound score for each associated successor label. This ensures that the head or first label element of the priority queue is associated with the highest factorizable upperbound score. It should be noted that the term factorizable refers to the ability to express the upperbound as a combination of terms each of which depends one exactly one position in the field. For example, in various exemplary embodiments according to this invention, operations such as addition, multiplication and the like may also be used to practice the invention. Control continues to step S 340 . 
   In step S 240 , the true score of the current best-label is determined. In one exemplary embodiment according to this invention, the true score is determined based on the formula for the field-label conditional field-feature probability as follows: 
                   f   ⁡     (       c   1     ,       c   2     ⁢     …c   L         )       =       p   ⁡     (       x   1     ,       x     2   ⁢               ⁢   …     ⁢           ,       x   L     |     c   1       ,     c   2     ,           ⁢   …   ⁢           ,     c   L       )       =       ∑     k   =   1     K     ⁢           ⁢       p   k     ⁢       ∏     l   =   1     L     ⁢           ⁢     p   ⁡     (         x   l     |     c   l       ,   k     )                       (   1   )               
for a field of L patterns with field features (x 1 ,x 2 , . . . x L ), where (c 1 ,c 2 , . . . c L ) denote a field label and where each pattern-label c 1  takes values 1 through C and there are K styles indexed by k=1 . . . K. Although the exemplary embodiment of this invention discusses the use of the field label conditional field-feature probability, it will be apparent that any known or later developed method of determining joint field-label probabilities may also be used in the practice of this invention. After the field label conditional field-feature probabilities are determined, control continues to step S 250 .
 
   In step S 250 , a factorizable upperbound score is determined for the first element or head of the priority queue. In one of the exemplary embodiments of this invention, the factorizable upperbound is determined based on a field-label conditional field-feature likelihood as follows: 
                   f   ⁡     (       c     1   ⁢               ⁢   …   ⁢           ⁢     c   L       )       ≤       max   k     ⁢       ∏     l   =   1     L     ⁢           ⁢     p   ⁡     (         x   l     |     c   l       ,   k     )                   (   2   )               
However, the max function in equation (2) can be moved to derive the equivalent inequality:
 
                   f   ⁡     (       c     1   ⁢               ⁢   …   ⁢           ⁢     c   L       )       ≤       ∏     l   =   1     L     ⁢           ⁢       max   k     ⁢     p   ⁡     (         x   l     |     c   l       ,   k     )                   (   3   )               
Inequality (3) then yields an easily factorizable upperbound for the true score for any field label. After the factorizable upperbound score is determined for the label at the head of the priority queue, control continues to step S 260 .
 
   In step S 260 , a determination is made whether the true score of the current-best label, determined in step S 240 , is less than the factorizable upperbound of the head element of the priority queue determined in step S 250 . If it is determined that the true score of the current-best label is less than the factorizable upperbound of the head element of the priority queue, control continues to step S 270 . Otherwise control continues to step S 340  where the best label is set equal to the current-best label. The best label is then returned as the most likely classification of the label. Control continues to step S 350  and the process immediately returns to step S 160  of  FIG. 2 . 
   In step S 270 , the value of the current label is set equal to the value of the head of the priority queue in step S 270 . Control then continues to step S 280 . In step S 280 , the head of the priority queue is removed from the queue. It will be apparent that any method of manipulating the elements of a queue or any data structure associated with maintaining an ordered list of candidate field labels may be used in this invention. Control continues to step S 290 . 
   The successors of the current label are determined in step S 290 . As discussed above, the successors of the current label are determined by increasing successive index positions by one position each time until the maximum number of classes is reached. Control continues to step S 300  where the adjusted priority queue is merged with the determined successors of the current label based on factorizable upperbound scores determined for each of the current label successors. Control continues to step S 310 . 
   In step S 310 , true scores the current label and the current-best label are determined based on formula (1) as discussed above. After the scores for the current field label and the current-best label are determined, control continues to step S 320 . 
   A determination is then made in step S 320  whether the true score for the current label is greater than the true score of the current-best label. If it is determined that the true score of the current label is less than or equal to the true score of the current-best label, control jumps immediately to step S 240  and the steps S 240 –S 320  are repeated. 
   If it is determined in step S 320  that the true score for the current label is greater than the true score for the current-best label, control continues to step S 330  where the current-best label is set equal to the current label. Otherwise, control then continues to step S 240 . The exemplary method for style conscious field classification ends when it is determined in step S 260  that the score for the current-best label is less than the factorizable upperbound of the head element of the priority queue. Control then continues to step S 350  and returns immediately to step S 170  of  FIG. 2  where the next pattern field is selected for classification. 
     FIG. 4  shows an exemplary style conscious field classification system  100 . The style conscious field classification system  100  comprises a processor  20 , a memory  30 , a successor determining circuit  40 , a true score determining circuit  50 , a priority queue head determining circuit  60 , a priority queue tail determining circuit  70 , a merging circuit  80  and a factorizable upperbound determining circuit  90  each connected to input/output circuit  10 . The style conscious field classification system  100  is connected via communications links  99  to the information repository  300  containing text image patterns  1000 , a web-enabled personal computer  400  containing text image patterns  1000 , a phone  500  and a web-enabled portable device  600 . 
   In one of the various exemplary embodiments according to this invention, the text image  1000  stored in web-enabled personal computer  400  is forwarded via communications links  99  to the style conscious field classification system  100  for the determination of recognized textual information. In various other exemplary embodiments according to this invention, the text image patterns  1000  contained in information repository  300  are forwarded via communications links  99  to the style conscious field classification system  100 . However, it will be apparent that the style conscious field classification system  100  may be located at any point accessible via communications links  99  or may be incorporated directly into a device such as phone  500  and/or web-enabled portable device  600 . 
   The processor  20  retrieves the text image  1000  from input/output circuit  10  of the style field classification system  100  and stores the text image  1000  in memory  30 . The processor  20  determines the first field in the text image  1000 . The contributions of candidate pattern labels to the factorizable upperbound score for each position within the field are determined. In various exemplary embodiments according to this invention, the contribution of each pattern label is stored in memory  30  as an array or the like. The processor  20  then optionally re-labels each candidate pattern based on the sorted index offsets as labels. The current label and current-best label data structures are initialized. For example, a list data structure may be used to store current label and current-best label information in an easily accessible data structure. In a first exemplary embodiment of this invention, lists associated with the current label and current-best label are initialized by assigning the values {1,1, . . . 1} 
   The priority queue is then initialized. As discussed above, the initialization of the priority queue may set the number of labels in the queue to zero by adding a NIL value indicator to the priority queue. It will be apparent that the current label, current-best label and priority queue data structures are merely exemplary and that any known or later developed data structure useful in holding and accessing the current label, current-best label and priority queue information may be used. 
   The processor  20  transfers the current label information stored in current label data structure of memory  30  to the successor determining circuit  40 . The successor determining circuit  40  determines successive labels based on the current label information. As discussed above, the current label successors are determined by successively increasing each index label by one until the maximum number of classes is reached. Thus for the current label {1,2,1} the determined successors would be {1+1), 2,1} and {1,2, (1+1)} which simplify to {2,2,1} and {1,2,2}. 
   The processor  20  then merges the determined successors of the current label by activating the merging circuit  80  with the priority queue information based on the factorizable upperbound score obtained by activating the factorizable upperbound determining circuit  90 . The true score determining circuit  50  is then activated to determine the true score value of the current-best label. The processor  20  activates the priority queue head determining circuit  60  to determine the head element of the priority queue data structure. The factorizable upperbound score determining circuit  90  is activated with the head element to determine a factorizable upperbound score for the field label. The processor  20  then compares the score of the current-best label to the factorizable upperbound score of the head element. 
   If processor  20  determines that the true score of the current-best label is greater than the factorizable upperbound of the head element, then the best label has been determined and processing ends. Otherwise, processor  20  sets the value of the current label data structure equal to the previously determined value of the head element of the priority queue. The head element of the priority queue is then removed from the priority queue. 
   The processor  20  activates the successor determining circuit  40  with the current label value stored in the current label data structure of memory  30 . The merging circuit  80  is then activated to merge the determined successors of the current label with the field labels in the priority queue. 
   The score determining circuit  50  is activated with the current label stored in the current label data structure of memory  30 . The true score determining circuit  50  is also activated with the current-best label stored in the current-best label data structure of memory  30 . The determined current label score and the current-best label score are then compared by processor  20 . 
   If processor  20  determines that the true score of the current label is greater than the true score of the current-best label, the value of the current-best label is set to the value of the current label. Otherwise, the processor  20  activates the true score determining circuit  50  with the newly determined current-best label, activates the factorizable upperbound determining circuit  90 , compares the true score of the current-best label to the factorizable upperbound of the head of the priority queue, sets the current label equal to the head of the priority queue, removes the head label from the priority queue, activates the successor determining circuit based on the value of the current label, merges the priority queue with the determined current label successors, compares determined scores of the current label and the current-best label, sets the current-best label equal to the current label if the true score of the current label is greater than the true score of the current-best label score. 
   This processor  20  continues the sequence until the true score of the current-best label is more than the factorizable upperbound of the head element of the priority queue. The best label is then set equal to the current-best label. The process continues for successive fields within the source patterns of text image  1000  until no further patterns remain to be processed. The cumulative determined best labels are the determined recognized text. 
     FIG. 5  shows an exemplary data structure for storing current label information according to this invention. The exemplary current label data structure  700  is comprised of a label portion  701  and associated upperbound portion  702 . For example, the current label data structure  700  contains current label {1,2,1} in the label portion  701 . The values indicate the best, second best and best candidate patterns. The current label data structure  700  contains an upperbound score of “0.07” in the upperbound portion  702 . The “0.07” value of the upperbound portion is the product form of the upperbound score obtained by adding, multiplying or performing some other separable operation on contributions of each associated pattern label to the upperbound score. It will be apparent that the upperbound scores are easily determined based on accumulations of previously determined upperbound scores associated with the respective candidate patterns for each position in the field. 
     FIG. 6  shows the exemplary determination of successor labels according to this invention. The first row is the label {1,1,1} of which the second, the third and the fourth rows form the successors. For example, the first position in the first row is incremented by one, until the maximum number of classes is reached. In the example, the number 2 is the maximum number of classes. The second position is then selected and similarly incremented until the maximum number of classes is reached. The third position selected and so on, for each field position. The determined labels form the successors of the label {1,1,1}. It will be apparent that the maximum number of classes may take various values depending on the pattern source and the patterns to be classified. 
     FIG. 7  shows an exemplary data structure for storing current-best label information according to this invention. The exemplary current-best label data structure  700  is comprised of a label portion  701  and associated upperbound portion  702 . For example, the current-best label data structure  700  contains current-best label {2,1,1} in the label portion  701 . These values indicate the current-best candidate field label is associated with the second best, best and best candidate patterns in the first, second and third positions of the candidate field. The current label data structure  700  contains an upperbound score of “0.3” in the upperbound portion  702 . The “0.3” value of the upperbound portion is the product form of the upperbound score. It will be apparent that the upperbound scores are easily determined based on accumulations of previously determined contributions to the upperbound score associated with each respective candidate pattern label. 
     FIG. 8  shows an exemplary data structure for storing priority queue information according to this invention. The priority queue data structure  1100  comprises label portions  701  and upperbound portions  702 . The priority queue data structure  1100  has a head portion  1101  or first element and a tail portion  1103 . The head portion  1101  of the priority queue data structure  1100  comprises a label portion  701  and an associated upperbound portion  702  containing the determined upperbound score for the first label portion  701 . 
   The tail portion  1103  of the priority queue data structure  1100  comprises field label portions  701  and associated upperbound portions  702  for each succeeding element of the priority queue data structure  1100  after the head element. The head portion  1101  and tail portion  1103  of the priority queue data structure  1100  are ordered based on the value of the associated upperbound portion  702 . As discussed above, the upperbound score is easily determined through accumulations of associated candidate patterns. 
     FIG. 9  shows an exemplary portion of a text image. Recognized and adjusted text is determined based on the text image  1000 . 
     FIG. 10  shows an exemplary portion of audio information  1001 . Recognized and adjusted audio information is determined based on the audio information  1001 . For example, the audio information  1001  contains a portion of a broadcast television commercial transcript. The recognized and adjusted text determined using this invention facilitates monitoring of a product advertising campaign by identifying “XYZ corporation” in the audio broadcast. 
     FIG. 11  shows an exemplary portion of video information  1002 . The Recognized and adjusted video object information is determined based on the video information  1002 . For example, video objects in a television broadcast are recognized and monitored for the “XYZ Corp” logo. 
     FIG. 12  shows an exemplary data structure for storing patterns associated with a field. For example, the label index associated with the field “ere” is shown. The first row contains the best candidate pattern labels for each position corresponding to a label of {1,1,1}. The second row corresponds to the second best candidate patterns for each position. The label associated with “ere” is {1,3,2}. 
     FIG. 13  shows an exemplary data structure for storing factorizable upperbound contribution values associated with patterns in a field, according to this invention. The first column portion of the exemplary data structure store the ordered position of the candidate pattern based on contributions to the upperbound score. For example, the first row contains all best candidate entries. The second column contains the values of the associate sorted candidate pattern labels for the first position. The third column is the contribution to the factorizable upperbound score of the second column value. The field position and contribution to upperbound score are repeated for each position in the current field. It will be apparent however, that various other data structures may also be used to store factorizable upperbound contribution values associated with the candidate pattern labels in a field without departing from the scope of this invention. 
   For example, the first row contains the ordered position value “1” indicating the best candidate pattern values. The best candidate pattern label in the first field position is “e” which makes a contribution of “0.99” to the upperbound score. Similarly the best candidate pattern label for the second field position is a “b” which makes a contribution of “0.95” to the factorizable upperbound. The best candidate pattern label for the third field position is an “a” which makes a contribution of “0.85” to the factorizable upperbound. As discussed above, in alternative implementations, cumulative factorizable upperbounds are easily determined for the label {1,1,1} by accumulating the corresponding values {0.99×0.95×0.85). As discussed above, the values may be accumulated using addition, multiplication or any suitable function without departing from the scope of this invention. 
   Each of the circuits  10 – 20  and  40 – 90  of the system for style conscious field classification  100  outlined above can be implemented as portions of a suitably programmed general-purpose computer. Alternatively,  10 – 20  and  40 – 90  of the system for style conscious field classification  100  outlined above can be implemented as physically distinct hardware circuits within an ASIC, or using a FPGA, a PDL, a PLA or a PAL, or using discrete logic elements or discrete circuit elements. The particular form each of the circuits  10 – 20  and  40 – 90  of the system for style conscious field classification  100  outlined above will take is a design choice and will be obvious and predictable to those skilled in the art. 
   Moreover, the system for style conscious field classification  100  and/or each of the various circuits discussed above can each be implemented as software routines, managers or objects executing on a programmed general purpose computer, a special purpose computer, a microprocessor or the like. In this case, the system for style conscious field classification  100  and/or each of the various circuits discussed above can each be implemented as one or more routines embedded in the communications network, as a resource residing on a server, or the like. The system for style conscious field classification  100  and the various circuits discussed above can also be implemented by physically incorporating the system for style conscious field classification  100  into a software and/or hardware system, such as the hardware and software systems of a web server or a client device. 
   As shown in  FIG. 3 , memory  30  can be implemented using any appropriate combination of alterable, volatile or non-volatile memory or non-alterable, or fixed memory. The alterable memory, whether volatile or non-volatile, can be implemented using any one or more of static or dynamic RAM, a floppy disk and disk drive, a write-able or rewrite-able optical disk and disk drive, a hard drive, flash memory or the like. Similarly, the non-alterable or fixed memory can be implemented using any one or more of ROM, PROM, EPROM, EEPROM, an optical ROM disk, such as a CD-ROM or DVD-ROM disk, and disk drive or the like. 
   The communication links  99  shown in  FIGS. 1 and 4  can each be any known or later developed device or system for connecting a communication device to the system for style conscious field classification  100 , including a direct cable connection, a connection over a wide area network or a local area network, a connection over an intranet, a connection over the Internet, or a connection over any other distributed processing network or system. In general, the communication links  99  can be any known or later developed connection system or structure usable to connect devices and facilitate communication 
   Further, it should be appreciated that the communication links  99  can be a wired or wireless links to a network. The network can be a local area network, a wide area network, an intranet, the Internet, or any other distributed processing and storage network. 
   While this invention has been described in conjunction with the exemplary embodiments outlined above, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, the exemplary embodiments of the invention, as set forth above, are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit and scope of the invention.