Title: Unsupervised key frame selection using information theory and colour histogram difference

Authors: Janya Sainui; Masashi Sugiyama

Addresses: Department of Computer Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand ' RIKEN Center for Advance Intelligence Project, University of Tokyo, Japan

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.

Keywords: key frame selection; similarity measure; information theory; quadratic mutual information; QMI; colour histogram difference.

DOI: 10.1504/IJBIDM.2020.106137

International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.3, pp.324 - 344

Received: 01 Dec 2016
Accepted: 23 Sep 2017

Published online: 13 Feb 2020 *

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