Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Content-Adaptive Analysis and Representation Framework for Audio Event Discovery from “Unscripted” Multimedia | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article 1D89013 Pages 1-24 DOI ASP 2006 89013 A Content-Adaptive Analysis and Representation Framework for Audio Event Discovery from Unscripted Multimedia Regunathan Radhakrishnan 1 Ajay Divakaran 1 Ziyou Xiong 1 and Isao Otsuka2 1 Mitsubishi Electric Research Laboratory Cambridge MA 02139 USA 2 Advanced Technology R D Center Mitsubishi Electric Corporation Hyogo 661-8661 Kyoto Japan Received 1 September 2004 Revised 21 April 2005 Accepted 4 May 2005 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 .
