10.1016/j.physa.2012.05.001

Bibtex

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

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

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 391(19):4487-4496, 2012

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. (C) 2012 Elsevier B.V. All rights reserved.