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Multi-q pattern analysis: A case study in image classification

Ricardo Fabbri, Wesley N. Gonçalves, Francisco J.P. Lopes and Odemir M. Bruno

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 19, 4487-4496

Abstract: This paper compares the effectiveness of the Tsallis entropy over the classic Boltzmann–Gibbs–Shannon entropy for general pattern recognition, and proposes a multi-q approach to improve pattern analysis using entropy. A series of experiments were carried out for the problem of classifying image patterns. Given a dataset of 40 pattern classes, the goal of our image case study is to assess how well the different entropies can be used to determine the class of a newly given image sample. Our experiments show that the Tsallis entropy using the proposed multi-q approach has great advantages over the Boltzmann–Gibbs–Shannon entropy for pattern classification, boosting image recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy and the multi-q approach.

Keywords: Image pattern classification; Texture; Tsallis entropy; Non-additive entropy (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:19:p:4487-4496

DOI: 10.1016/j.physa.2012.05.001

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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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