6 March 2015 Enhanced features for supervised lecture video segmentation and indexing
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Abstract
Lecture videos are common and increase rapidly. Consequently, automatically and efficiently indexing such videos is an important task. Video segmentation is a crucial step of video indexing that directly affects the indexing quality. We are developing a system for automated video indexing and in this paper discuss our approach for video segmentation and classification of video segments. The novel contributions in this paper are twofold. First we develop a dynamic Gabor filter and use it to extract features for video frame classification. Second, we propose a recursive video segmentation algorithm that is capable of clustering video frames into video segments. We then use these to classify and index the video segments. The proposed approach results in a higher True Positive Rate(TPR) 89.5% and lower False Discovery Rate(FDR) 11.2% compared with the commercial system(TPR= 81.8%, FDR=39.4%) demonstrate that the performance is significantly improved by using enhanced features.
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Di Ma, Gady Agam, "Enhanced features for supervised lecture video segmentation and indexing", Proc. SPIE 9408, Imaging and Multimedia Analytics in a Web and Mobile World 2015, 940809 (6 March 2015); doi: 10.1117/12.2083475; https://doi.org/10.1117/12.2083475
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