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

Abstract This paper presents a novel color segmentation technique using fuzzy co-clustering approach in which both the objects and the features are assigned membership functions. An objective function which includes a multi-dimensional distance function as the dissimilarity measure and entropy as the regularization term is formulated in the proposed fuzzy co-clustering for images (FCCI) algorithm. The chrominance color cues a ⁎ and b ⁎ of CIELAB color space are used as the feature variables for co-clustering. The experiments are conducted on 100 natural images obtained from the Berkeley segmentation database. It is observed from the experimental results that the proposed FCCI yields well formed, valid and high quality clusters, as verified from Liu’s F -measure and Normalized Probabilistic RAND index. The proposed color segmentation method is also compared with other segmentation methods namely Mean-Shift, NCUT, GMM, FCM and is found to outperform all the methods. The bacterial foraging global optimization algorithm gives image specific values to the parameters involved in the algorithm.