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

Author

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

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.

Suggested Citation

  • Fabbri, Ricardo & Gonçalves, Wesley N. & Lopes, Francisco J.P. & Bruno, Odemir M., 2012. "Multi-q pattern analysis: A case study in image classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4487-4496.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:19:p:4487-4496
    DOI: 10.1016/j.physa.2012.05.001
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    References listed on IDEAS

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    1. Barbieri, Andre L. & de Arruda, G.F. & Rodrigues, Francisco A. & Bruno, Odemir M. & Costa, Luciano da Fontoura, 2011. "An entropy-based approach to automatic image segmentation of satellite images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(3), pages 512-518.
    2. Tsallis, Constantino & Stariolo, Daniel A., 1996. "Generalized simulated annealing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 233(1), pages 395-406.
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    Cited by:

    1. Ben Ishak, Anis, 2017. "Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 521-536.
    2. Amelia Carolina Sparavigna, 2015. "Tsallis Entropy In Bi-level And Multi-level Image Thresholding," International Journal of Sciences, Office ijSciences, vol. 4(01), pages 40-49, January.
    3. Fabbri, Ricardo & Bastos, Ivan N. & Neto, Francisco D. Moura & Lopes, Francisco J.P. & Gonçalves, Wesley N. & Bruno, Odemir M., 2014. "Multi-q pattern classification of polarization curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 332-339.
    4. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.

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