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 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.
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Choi, Y., Kim, K. (2004). Video Summarization Using Fuzzy One-Class Support Vector Machine. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24707-4_7
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DOI: https://doi.org/10.1007/978-3-540-24707-4_7
Publisher Name: Springer, Berlin, Heidelberg
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