We present video summarization and indexing techniques using the MPEG-7 motion activity descriptor. The descriptor can be extracted in the compressed domain and is compact, and hence is easy to extract and match. We establish that the intensity of motion activity of a video shot is a direct indication of its summarizability. We describe video summarization techniques based on sampling in the cumulative motion activity space. We then describe combinations of the motion activity based techniques with generalized sound recognition that enable completely automatic generation of news and sports video summaries. Our summarization is computationally simple and flexible, which allows rapid generation of a summary of any desired length.This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. {ajayd,peker,regu,zxiong,romain}@merl.com AbstractWe present video summarization and indexing techniques using the MPEG-7 motion activity descriptor. The descriptor can be extracted in the compressed domain and is compact, and hence is easy to extract and match. We establish that the intensity of motion activity of a video shot is a direct indication of its summarizability. We describe video summarization techniques based on sampling in the cumulative motion activity space. We then describe combinations of the motion activity based techniques with generalized sound recognition that enable completely automatic generation of news and sports video summaries. Our summarization is computationally simple and flexible, which allows rapid generation of a summary of any desired length.

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In our past work we have used supervised audio classification to develop a common audio-based platform for highlight extraction that works across three diEerent sports. We then use a heuristic to post-process the classification results to identify interesting events and also to adjust the summary length. In this paper, we propose a combination of unsupervised and supervised learning approaches to replace the heuristic. The proposed unsupervised framework mines the semantic audio-visual labels so as to detect "interesting" events. We then use a Hidden Markov Model based approach to control the length of the summary. Our experimental results show that the proposed techniques are promising.

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.