Unsupervised key frame selection using information theory and colour histogram difference
by Janya Sainui; Masashi Sugiyama
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 16, No. 3, 2020

Abstract: Key frame selection is one of the important research issues in video content analysis, as it helps effective video browsing and retrieval as well as efficient storage. Key frames would typically be as different from each other as possible but, at the same time, cover the entire content of the video. However, the existing methods still lose some meaningful frames due to an inaccurate evaluation of the differences between frames. To address this issue, in this paper, we propose a novel method of key frame selection which incorporates an information theoretic measure, called quadratic mutual information (QMI), with the colour histogram difference. Here, these two criteria are used to produce an appropriate frame difference measure. Through the experiments, we demonstrate that the proposed key frame selection method generates a more coverage of the entire video content with minimum redundancy of key frames compared with the competing approaches.

Online publication date: Wed, 01-Apr-2020

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