Multi-q Analysis of Image Patterns

This paper studies the use of the Tsallis Entropy versus the classic Boltzmann-Gibbs-Shannon entropy for classifying image patterns. Given a database of 40 pattern classes, the goal is to determine the class of a given image sample. Our experiments show that the Tsallis entropy encoded in a feature...

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Bibliographic Details
Published in:arXiv.org 2011-12
Main Authors: Fabbri, Ricardo, Gonçalves, Wesley N, Lopes, Francisco J P, Bruno, Odemir M
Format: Article
Language:English
Subjects:
Coding
Entropy
Entropy (Information theory)
Image classification
Pattern recognition
Online Access:Get full text
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Summary:This paper studies the use of the Tsallis Entropy versus the classic Boltzmann-Gibbs-Shannon entropy for classifying image patterns. Given a database of 40 pattern classes, the goal is to determine the class of a given image sample. Our experiments show that the Tsallis entropy encoded in a feature vector for different \(q\) indices has great advantage over the Boltzmann-Gibbs-Shannon entropy for pattern classification, boosting recognition rates by a factor of 3. We discuss the reasons behind this success, shedding light on the usefulness of the Tsallis entropy.
ISSN:2331-8422