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Tsallis entropy-based information measures for shot boundary detection and keyframe selection

Automatic shot boundary detection and keyframe selection constitute major goals in video processing. We propose two different information-theoretic approaches to detect the abrupt shot boundaries of a video sequence. These approaches are, respectively, based on two information measures, Tsallis mutual information and Jensen-Tsallis divergence, that are used to quantify the similarity between two frames. Both measures are also used to find out the most representative keyframe of each shot. The representativeness of a frame is basically given by its average similarity with respect to the other frames of the shot. Several experiments analyze the behavior of the proposed measures for different color spaces (RGB, HSV, and Lab), regular binnings, and entropic indices. In particular, the Tsallis mutual information for the HSV and Lab color spaces with only 8 regular bins for each color component and an entropic index between 1. 5 and 1. 8 substantially improve the performance of previously proposed methods based on mutual information and Jensen-Shannon divergence

This work has been funded in part by grants from the Spanish Government (Nr. TIN2010-21089-C03-01), from the Catalan Government (Nr. 2009-SGR-643 and Nr. 2010-CONE2-00053), and from the Natural Science Foundation of China (61179067, 61103005, 60879003)

漏 Signal, Image and Video Processing, 2013, vol. 7, n煤m. 3, p. 507-520

Springer Verlag

Author: Vila Duran, Marius
Bardera i Reig, Antoni
Xu, Qing
Feixas Feixas, Miquel
Sbert, Mateu
Date: 2013 March 15
Abstract: Automatic shot boundary detection and keyframe selection constitute major goals in video processing. We propose two different information-theoretic approaches to detect the abrupt shot boundaries of a video sequence. These approaches are, respectively, based on two information measures, Tsallis mutual information and Jensen-Tsallis divergence, that are used to quantify the similarity between two frames. Both measures are also used to find out the most representative keyframe of each shot. The representativeness of a frame is basically given by its average similarity with respect to the other frames of the shot. Several experiments analyze the behavior of the proposed measures for different color spaces (RGB, HSV, and Lab), regular binnings, and entropic indices. In particular, the Tsallis mutual information for the HSV and Lab color spaces with only 8 regular bins for each color component and an entropic index between 1. 5 and 1. 8 substantially improve the performance of previously proposed methods based on mutual information and Jensen-Shannon divergence
This work has been funded in part by grants from the Spanish Government (Nr. TIN2010-21089-C03-01), from the Catalan Government (Nr. 2009-SGR-643 and Nr. 2010-CONE2-00053), and from the Natural Science Foundation of China (61179067, 61103005, 60879003)
Format: application/pdf
ISSN: 1863-1711 (versi贸 electr貌nica)
1863-1703
1863-1703 (versi贸 paper)
Document access: http://hdl.handle.net/10256/11682
Language: eng
Publisher: Springer Verlag
Collection: MICINN/PN 2011-2013/TIN2010-21089-C03-01
AGAUR/2009-2014/2009 SGR-643
AGAUR/2011-2013/2010 CONE2 00053
Reproducci贸 digital del document publicat a: http://dx.doi.org/10.1007/s11760-013-0452-3
Articles publicats (D-IMA)
Is part of: 漏 Signal, Image and Video Processing, 2013, vol. 7, n煤m. 3, p. 507-520
Rights: Tots els drets reservats
Subject: Informaci贸, Teoria de la
Information theory
Entropia (Teoria de la informaci贸)
Entropy (Information theory)
Imatge -- Processament
Image processing
Title: Tsallis entropy-based information measures for shot boundary detection and keyframe selection
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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