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Video Summarization Using Importance-based Fuzzy One-Class Support Vector Machine  

Kim, Ki-Joo (한국항공대학교 대학원 컴퓨터공학과)
Choi, Young-Sik (한국항공대학교 항공전자 및 정보통신공학부)
Publication Information
Journal of Internet Computing and Services / v.12, no.5, 2011 , pp. 87-100 More about this Journal
Abstract
In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.
Keywords
Importance-based Fuzzy One-class SVM; IFOC SVM; One-class SVM; SVM; video summarization;
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