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Entropy 2016, 18(3), 73; doi:10.3390/e18030073

Selecting Video Key Frames Based on Relative Entropy and the Extreme Studentized Deviate Test

1
School of Computer Science and Technology, Tianjin University, Yaguan Road #135, 300350 Tianjin, China
2
Graphics and Imaging Lab, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain
This paper is an extended version of the paper entitled “Key Frame Selection Based on KL-Divergence”, presented at IEEE International Conference on Multimedia Big Data (BigMM 2015), Beijing, China, 20–22 April 2015.
*
Authors to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 11 January 2016 / Revised: 31 January 2016 / Accepted: 15 February 2016 / Published: 9 March 2016

Abstract

This paper studies the relative entropy and its square root as distance measures of neighboring video frames for video key frame extraction. We develop a novel approach handling both common and wavelet video sequences, in which the extreme Studentized deviate test is exploited to identify shot boundaries for segmenting a video sequence into shots. Then, video shots can be divided into different sub-shots, according to whether the video content change is large or not, and key frames are extracted from sub-shots. The proposed technique is general, effective and efficient to deal with video sequences of any kind. Our new approach can offer optional additional multiscale summarizations of video data, achieving a balance between having more details and maintaining less redundancy. Extensive experimental results show that the new scheme obtains very encouraging results in video key frame extraction, in terms of both objective evaluation metrics and subjective visual perception. View Full-Text
Keywords: relative entropy; square root of relative entropy; extreme Studentized deviate test; video key frame selection; multiscale key frames; wavelet video relative entropy; square root of relative entropy; extreme Studentized deviate test; video key frame selection; multiscale key frames; wavelet video
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guo, Y.; Xu, Q.; Sun, S.; Luo, X.; Sbert, M. Selecting Video Key Frames Based on Relative Entropy and the Extreme Studentized Deviate Test. Entropy 2016, 18, 73.

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