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

Author(s)

Fabbri, Ricardo ^[1] ; Goncalves, Wesley N. ^[2] ; Lopes, Francisco J. P. ^[3] ; Bruno, Odemir M. ^[2]

Affiliation

^[1] Univ Estado Rio de Janeiro, Inst Politecn Rio de Janeiro, BR-28601970 Nova Friburgo, RJ - Brazil

^[2] Univ Sao Paulo, IFSC, BR-13560970 Sao Carlos, SP - Brazil

^[3] Univ Fed Rio de Janeiro, Inst Biofis Carlos Chagas Filho, BR-21941902 Rio De Janeiro, RJ - Brazil

• Document type: Journal article
• Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 391, n. 19, p. 4487-4496, OCT 1 2012.
• Web of Science Citations: 7

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

• FAPESP's process: 11/01523-1 - Computer vision methods applied to the identification and analysis of plants
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Regular Research Grants
• FAPESP's process: 10/08614-0 - Static and Dynamic Texture Analysis and their Applications in Biology and Nanotechnology
• Grantee: Wesley Nunes Gonçalves
• Support Opportunities: Scholarships in Brazil - Doctorate