Fundamentals of speech recognition
Fundamentals of speech recognition
Automatic text recognition for video indexing
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
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MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Video Summaries through Mosaic-Based Shot and Scene Clustering
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Extracting Actors, Actions and Events from Sports Video - A Fundamental Approach to Story Tracking
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Content-based video analysis, indexing and representation using multimodal information
Content-based video analysis, indexing and representation using multimodal information
Creating audio keywords for event detection in soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
A statistical framework for fusing mid-level perceptual features in news story segmentation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Detection of slow-motion replay segments in sports video for highlights generation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Graph-based multilevel temporal segmentation of scripted content videos
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Video scene detection using graph-based representations
Image Communication
Dominant sets based movie scene detection
Signal Processing
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Frontiers of Computer Science: Selected Publications from Chinese Universities
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We propose a content-adaptive analysis and representation framework to discover events using audio features from "unscripted" multimedia such as sports and surveillance for summarization. The proposed analysis framework performs an inlier/outlier-based temporal segmentation of the content. It is motivated by the observation that "interesting" events in unscripted multimedia occur sparsely in a background of usual or "uninteresting" events. We treat the sequence of low/mid-level features extracted from the audio as a time series and identify subsequences that are outliers. The outlier detection is based on eigenvector analysis of the affinity matrix constructed from statistical models estimated from the subsequences of the time series. We define the confidence measure on each of the detected outliers as the probability that it is an outlier. Then, we establish a relationship between the parameters of the proposed framework and the confidence measure. Furthermore, we use the confidence measure to rank the detected outliers in terms of their departures from the background process. Our experimental results with sequences of low- and mid-level audio features extracted from sports video show that "highlight" events can be extracted effectively as outliers from a background process using the proposed framework. We proceed to show the effectiveness of the proposed framework in bringing out suspicious events from surveillance videos without any a priori knowledge. We show that such temporal segmentation into background and outliers, along with the ranking based on the departure from the background, can be used to generate content summaries of any desired length. Finally, we also show that the proposed framework can be used to systematically select "key audio classes" that are indicative of events of interest in the chosen domain.