Multi- pattern analysis: A case study in image classification

This paper compares the effectiveness of the Tsallis entropy over the classic BoltzmannaGibbsaShannon 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 pat...

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Bibliographic Details
Published in:Physica A 2012-10, Vol.391 (19), p.4487-4496
Main Authors: Fabbri, Ricardo, Goncalves, Wesley N, Lopes, Francisco JP, Bruno, Odemir M
Format: Article
Language:English
Subjects:
Bolts
Classification
Entropy
Image classification
Pattern analysis
Pattern recognition
Samples
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Summary:This paper compares the effectiveness of the Tsallis entropy over the classic BoltzmannaGibbsaShannon 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 BoltzmannaGibbsaShannon 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.
ISSN:0378-4371
DOI:10.1016/j.physa.2012.05.001