nils-herrmann/clean_scirepeval huggingface dataset:
Personalized abstraction of broadcasted American football video by highlight selection

Video abstraction is defined as creating shorter video clips or video posters from an original video stream. In this paper, we propose a method of generating a personalized abstract of broadcasted American football video. We first detect significant events in the video stream by matching textual overlays appearing in an image frame with the descriptions of gamestats in which highlights of the game are described. Then, we select highlight shots which should be included in the video abstract from those detected events reflecting on their significance degree and personal preferences, and generate a video clip by connecting the shots augmented with related audio and text. An hour-length video can be compressed into a minute-length personalized abstract. We experimentally verified the effectiveness of this method by comparing man-made video abstracts.
Interactive Clustering of Video Segments for Media Structuring

Structuring video data is necessary for its effective retrieval and summarization. In particular, collecting similar scenes from semantic aspects highly contributes to the structuring. In this paper, we propose a method of clustering the scenes with relevance feedback, which may be able to bridge the gap between the video data and its semantics. First, spatio-temporal video segments of a fixed length are clustered according to image features of each segment. Then, a user performs feedback to the results of clustering, whether each segment is relevant to the cluster it belongs. The clustering accuracy can be improved through the interaction based on the feedback information. For diverse kinds of video streams, we investigated how the feedback should be given and demonstrated the effectiveness of the interactive clustering
Incremental acquisition of knowledge about layout structures from examples of documents

Document image analysis systems often utilize the knowledge about layout structures to extract layout objects labeled logically. However, the lack of the facility for knowledge acquisition limits the applicability of the systems. The authors propose a method of acquiring knowledge for document image analysis. Given examples of document images and their layout objects labeled logically, the method generates and modifies the knowledge. The method is incremental so that the knowledge can be efficiently modified using additional examples. Counterexamples generated as errors obtained from the analysis of an example image can also be reflected into the knowledge so that the system may no longer generate the errors. Experimental results on both knowledge acquisition and the analysis using the acquired knowledge are also presented.<<ETX>>
Automatic parsing of American football videos by intermodal collaboration based on transition rules

This paper proposes an automatic American football video parsing method based on transition rules of an American football game. Combining the results of live scene extraction and superimposed text detection based on image features enables us to segment the video into play units of a game. Temporally associating the segmented play units with the detected superimposed texts and the closed-caption text attaches possible semantic content information to the play units. Finally, selecting only the play units which conform to transition rules of the sports game from the obtained play unit sequence, while discarding or complementing unnecessary or insufficient play units and attached semantic content information, realizes the semantic video parsing.
Generation of personalized abstract of sports video

Video abstraction is defined as creating a shorter video clip from an original video stream. In this paper, we propose a method of generating a personalized abstract of broadcasted sports video. We first detect significant events from the video stream by matching with gamestats in which highlights of the game are described. Textual information in an overlay appearing on an image frame is recognized for this matching. Then, we select highlight shots from these detected events, reflecting on personal preferences. Finally, we connect each shot augmented with related audio and text in temporal order. From experimental results, we verified that an hourlength video can be compressed into a minute-length personalized abstract.
Determining Recording Location Based on Synchronization Positions of Audiowatermarking

In this paper, we propose a novel application of digital watermarking, determination of recording locations. This application enables us to determine the seat location in an auditorium where a recording was made. Precisely measured synchronization positions of the spread-spectrum watermarks are used for the determination. To avoid use of mismeasured synchronization positions, the algorithm discards synchronization positions with the corresponding normalized correlation values below a threshold. The experiments with our implementation resulted in accurate determinations; almost all of the locations can be determined within the error of 0.5 m. These experimental results successfully show the potential applicability of our application
Facial expression preserving privacy protection using image melding

An enormous number of images are currently shared through social networking services such as Facebook. These images usually contain appearance of people and may violate the people's privacy if they are published without permission from each person. To remedy this privacy concern, visual privacy protection, such as blurring, is applied to facial regions of people without permission. However, in addition to image quality degradation, this may spoil the context of the image: If some people are filtered while the others are not, missing facial expression makes comprehension of the image difficult. This paper proposes an image melding-based method that modifies facial regions in a visually unintrusive way with preserving facial expression. Our experimental results demonstrated that the proposed method can retain facial expression while protecting privacy.
Hierarchical Anomality Detection Based on Situation

In this paper, we propose a novel anomality detection method based on external situational information and hierarchical analysis of behaviors. Past studies model normal behaviors to detect anomality as outliers. However, normal behaviors tend to differ by situations. Our method combines a set of simple classifiers with pedestrian trajectories as inputs. As mere path information is not sufficient for detecting anomality, trajectories are first decomposed into hierarchical features of different abstract levels and then applied to appropriate classifiers corresponding to the situation it belongs to. Effects of the methods are tested using real environment data.
PlayWatch: chart-style video playback interface

This paper proposes the chart-style video playback interface PlayWatch; it displays a chart of semantic indices for locating video scenes. The main features of PlayWatch are: 1) the user understands the distribution of the scenes because PlayWatch shows the indices in order. 2) The user can access a desired scene directly through the indices since they also act as link buttons. This paper also describes the evaluation of PlayWatch. Experiments on scene searching show that PlayWatch is effective in accessing precisely indexed scenes.
Real-Time User Position Estimation in Indoor Environments Using Digital Watermarking for Audio Signals

In this paper, we propose a method for estimating the user position where a user is holding a microphone in an indoor environment using digital watermarking for audio signals. The proposed method utilizes detection strengths, which are calculated while detecting spread-spectrum-based watermarks. Taking into account delays and attenuation of the watermarked signals emitted from multiple loudspeakers and other factors, we construct a model of detection strengths. The user position is estimated in real-time using the model. The experimental results indicate that the user positions are estimated with 1.3 m of root mean squared error on average for the case where the user is static. We demonstrate that the proposed method successfully estimates the user position even when the user moves.
Markov random field-based real-time detection of intentionally-captured persons

Most videos taken by videographers contain intentionally-captured persons (ICPs), who are essential for what the videographers want to express in their video. This paper presents a method to detect ICPs in real-time. Whether a person in a video is an ICP or not is reflected in features such as the person's motion and camera motion, which are thus beneficial for detecting ICPs. However, estimating camera motion is computationally expensive. For real-time detection, we use samples of acceleration and angular velocity obtained from inertial sensors instead of estimating camera motion. Considering that pairwise constraints based on differences between persons' sizes also improve the detection performance, we model the ICPs using Markov random field. We experimentally evaluate the performance of our method and demonstrate that it works in real-time.
On-line Geospatial Term Extraction from Streaming Geotagged Tweets

Recently, geotagged posts to social media such as Twitter have become major sources for geospatial information referenced to a set of geographic coordinates. Especially, many existing work collects geospatial terms which identify geographic locations by examining the spatial locality of the term usage patterns observed in the geotagged posts accumulated for a certain period of time. Although the spatial locality of the collected geospatial terms can only be temporary, such time variability difference among the geospatial terms have not been considered in the existing work. Thus, in order to separately collect the stationary and temporary geospatial terms with proper timing, we propose an on-line method for constructing a geographical dictionary containing the up-to-date geospatial terms and their locations by continuously examining the spatial locality of terms in streaming geotagged posts. The geospatial terms can be distinguished between stationary and temporary terms according to their usage patterns in the recent set of geotagged posts and their locations recorded in the geographical dictionary. Additionally, images representing each geospatial term can also be collected from the posts containing the corresponding term. The usefulness of the collected geospatial information is demonstrated in comparison to existing geographical dictionaries constructed by experts, crowdsourcing, and batch method.
Constructing Geospatio-Temporal Concept Graphs from Tagged Images

This paper proposes a method for discovering the knowledge about geospatio-temporal concepts, which are strongly related to specific locations and times, from images tagged with latitude and longitude coordinates, time-stamps, and textual tags. The proposed method firstly extracts textural tags used either in specific locations or times as the geospatial or temporal concepts with their location or time information. Then, the semantic relations among the geospatio-temporal concepts are extracted based on the co-occurrence of the corresponding tags to be represented as a graph. Finally, visually discriminative geospatio-temporal concepts are linked to representative visual concepts. The usefulness of the discovered knowledge is evaluated based on the automatic tagging for the images tagged with latitude and longitude coordinates and time-stamps.
Example-based video remixing for home videos

Video remixes are generally created by sequentially arranging selected video clips and combining them with other media streams such as audio clips. In this paper, an example-based approach is adopted for semi-automatically creating video remixes of good expressive quality from home videos. Given multiple home video clips and audio clips, the proposed system generates a video remix by four processes: I)video clip sequence generation, II)audio clip selection, III)audio boundary extraction, and IV)video segment extraction based on professionally created video remix examples. A user interface which presents video clips according to their suitability to the examples and their perceived quality is developed so that users can efficiently and effectively select and arrange suitable video clips in video clip sequence generation. By using movie trailers of action genre as video remix examples, a 43-second video remix was created from 45-minute home videos and was subjectively evaluated better than the one created considering only the perceived quality of video clips.
Towards abstracting sports video by highlights

Recently, video abstraction has become a demanding application in multimedia computing. It is defined as creating shorter video clips or video posters from the original video stream. We present a basic approach towards abstracting sports video by highlights, dealing with American football games. Using event based indexing, we create an abstracted video clip automatically. To select the appropriate highlights of the game, an impact factor reflecting on the importance of the event is newly introduced. It was possible to make an approximate 5-minute clip from the 3-hour original video.
Mobile robot navigation by user-friendly goal specification

In this article, we discuss mobile robot navigation in an unknown environment. Different from the conventional formulation of the problem, we assume that the position of the goal is not given by its coordinates defined with the environment but by a verbal description of the route to it, such as "turn right at the 2nd corner, and...". We call distinctive places such as intersections, which are used to verbally describe a route, "landmarks" in a broad sense. Since it is difficult to give the robot landmark models that guarantee correct landmark recognition, we consider positional relations among landmarks given by the description, of the road to disambiguate the result of each landmark recognition. In addition, we try to make the robot learn the correct landmark models from the results of the previous navigation.
Model based system for analyzing document images

Document image analysis is the process of deriving logically structured representation of a document by analyzing the layout structure of its image. This paper proposes a knowledge based system for document image analysis which is applicable to various kinds of documents. The characteristics of the system are as follows: (1) The knowledge base called document model encodes only object-level knowledge hierarchically, declaratively and symbolically, aiming at high expressivity and maintainability of the knowledge description; (2) the document model is automatically constructed by referring samples of document images, and incrementally refined by feedback of error information of analysis.<<ETX>>
Effect of personalization on retrieval and summarization of sports video

Personalization is one of the most important mechanisms to make multimedia systems easy to use. In video applications, its embodiment is to tailor video contents for a particular viewer. For this purpose, we are now developing a system of retrieving and browsing video segments, called video portal with personalization (VIPP). VIPP is characterized by 1) supporting the viewer's access to video contents and making a summarized video clip by taking his/her preference into account, and 2) acquiring the viewer's profile from his/her operations automatically. In this paper, we discuss the effect of personalization on retrieval and summarization of sports videos on VIPP.
Detection of groups in crowd considering their activity state

In this paper, we focus on the problem of group detection in crowd, which is a task of partitioning a set of pedestrians in a scene into small subsets called groups based on their trajectories. Most of previous methods use only a single model for representing a relationship between trajectories of pedestrians who belong to the same group. However, such relationship would vary depending on the activity state (e.g. walking together, approaching, splitting, and so on) of the group. In this paper, we propose a novel group detection method which can cope with a variation of groups' activity state. The proposed method constructs different models for each activity state in order to appropriately evaluate the relationship of pedestrians' trajectories. In addition, our method regards groups' activity state as hidden variables and estimates their probability distributions, which is used for integrating the constructed models. The proposed method outperforms existing methods in the experiment on the public dataset.
Audio-Based Estimation of Speakers Directions for Multimedia Meeting Logs

This paper is concerned with an audio-based method for estimating speaker directions in meeting environment. It is well-known that cross-power spectrum phase (CSP) analysis is a very powerful tool for localizing sound sources. However, when we adopt the CSP-based method together with a circular microphone array system to estimate the speaker directions in 360-degree range (e.g. round-table discussions), the method fails to estimate the directions due to the existence of imaginary peaks of CSP coefficients. In order to circumvent the above problem, we propose a method to suppress the imaginary peaks, which uses a circular-array version of the method proposed by Nishiura and appropriate scaling around the imaginary peaks. Experimental results are also shown to demonstrate the effectiveness of the proposed method.
Breaking the YASS algorithm via pixel and DCT coefficients analysis

In this paper, we present a steganalytic method that can reliably detect messages hidden in JPEG images using the steganographic algorithm YASS, which is a JPEG steganographic method shown to be undetectable using current best blind steganalysis classifiers. The key element of the method is features extracted from the imagepsilas pixels and DCT coefficients. Although the YASS process effectively disables the calibration based and the noise model based JPEG steganalyzers, it also disturbs the pixels and DCT coefficients dependency after the secret message embedding. An SVM based classifier is trained based on the extracted features for the detection of the presence of steganography. The method is tested on a diverse set of test images that include both originally uncompressed and compressed images in the TIF and JPEG formats. Our experimental results have demonstrated that the proposed steganalyzers can reliably break YASS.
Delivery method for viewer-specific privacy protected video using discrete wavelet transform

This paper presents a delivery method for viewer-specific privacy protected video, where “viewer-specific” implies that the way of privacy protection (e.g., box, mosaic, transparency) varies according to viewer's authority. In this method, in order to reduce the load of a delivery server, viewer-specific privacy protected videos are not produced at the delivery server, but they are produced at each viewer's terminal. The delivery server extracts and decomposes the information of human objects, and then embeds the information into background images using information hiding technique. Each viewer extracts a part of the decomposed information of human objects according to his/her authority, and then produces privacy protected video by integrating the extracted information. In this method, the discrete wavelet transform (DWT) plays a key role. The proposed method is evaluated through experiments.
Weighted stego-Image based steganalysis in multiple least significant bits

The paper proposes a method for LSB steganalysis of images, where the secret message is embedded in a given number L of the least significant bits. The proposed estimation method is based on weighted stego-Image and no assumption of cover images is required. The method can detect the presence of stego message using LSB steganography from the case L = 1 to arbitrary L>1.The estimation formula is clean and computation complexity is low. To evaluate the proposed steganalytic method, two experiments of detection and estimation are performed. It is shown that the accuracy of detecting the existence of secret messages in images and of estimating the embedding ratio of secret messages is relatively high.
Video clustering using spatio-temporal image with fixed length

In order to handle video media such as TV programs efficiently and effectively, we need to segment a video stream into video segments and structuralize them based on their contents. We focus on similarity, which is one of the important relations between video segments, and describe a method to cluster similar segments in a video stream. The conventional clustering methods are based on shots, but no complete method to detect shot boundaries has yet been established. Our method is based on fixed length video stream segments, called video packets. Generating spatio-temporal images, we employ cooccurrence matrices to express features in the time dimension explicitly. From clustering experiments for actual TV programs, we obtained clustering accuracy of 81%.
An Improved Steganalysis Method of LSB Matching

In this paper, we propose an improved steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. Firstly we propose run length based features, which are sensitive to LSB matching in spatial domain of images. At the same time, we extend Kerpsilas features, the statistical moments, to higher orders, which are sensitive to LSB matching in transform domain. Then the improved method is constructed based on these features. Experimental results on two datasets demonstrate that this method has superior results compared with other recently proposed algorithms, and shows that the proposed method is efficient to detect the LSB matching steganography on compressed or uncompressed images.
Real-Time Local Word Database Construction from Twitter

Recently, geotagged posts to social media such as Twitter have been used to automatically construct a geographical dictionary containing diverse types of local words which indicate specific locations in the real world. The existing methods typically examine the spatial locality of the usage patterns observed in the geotagged posts accumulated for a certain period of time to select the local words, however, how long the geotagged posts need to be accumulated depends on the usage frequency of the word, and additionally, some local words can indicate different locations at different times. Thus, we propose a real-time method for constructing a local word database which consistently keeps the local words and their locations up to date by iteratively adding new local words, removing old temporary local words, and updating the locations indicated by the local words. These functions are realized by adaptively recording/resetting the usage history of each word to properly examine its spatial locality and by assigning the weight for each geotag which is used to represent the locations indicated by the local words according to their temporal variations. The local word database constructed by our proposed method was verified to contain more up-todate local words and locations compared to other types of geographical dictionary constructed by experts, crowdsourcing, and from the geotagged tweets accumulated for a fixed period of time based on the performance evaluations of tweet location estimation as an example of applications utilizing the geographical dictionaries.