A non-extensive entropy feature and its application to texture classification

TL;DR

This paper introduces a novel non-extensive entropy feature based on Gaussian information for texture classification, demonstrating superior accuracy and effectiveness over traditional entropies in representing correlated textures.

Contribution

It presents a new non-extensive entropy measure that is bounded, non-additive, and effective for texture analysis, outperforming existing entropy measures.

Findings

High classification accuracy achieved with the new entropy.

Superior performance over Shannon, Renyi, Tsallis, and Pal entropies.

Effective representation of correlated textures using the proposed entropy.

Abstract

This paper proposes a new probabilistic non-extensive entropy feature for texture characterization, based on a Gaussian information measure. The highlights of the new entropy are that it is bounded by finite limits and that it is non additive in nature. The non additive property of the proposed entropy makes it useful for the representation of information content in the non-extensive systems containing some degree of regularity or correlation. The effectiveness of the proposed entropy in representing the correlated random variables is demonstrated by applying it for the texture classification problem since textures found in nature are random and at the same time contain some degree of correlation or regularity at some scale. The gray level co-occurrence probabilities (GLCP) are used for computing the entropy function. The experimental results indicate high degree of the classification accuracy. The performance of the new entropy function is found superior to other forms of entropy such as Shannon, Renyi, Tsallis and Pal and Pal entropies on comparison. Using the feature based polar interaction maps (FBIM) the proposed entropy is shown to be the best measure among the entropies compared for representing the correlated textures.