  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.
