A Multilabel Texture Segmentation Based on Local Entropy Signature

We propose a multilabel segmentation that aims to partition a texture image into multiple regions based on a homogeneity condition using local entropy measured at varying scales. For multi-label segmentation, a bipartitioning segmentation scheme is recursively applied to confined regions obtained by previous segmentation steps. The empirical entropy is measured in the local neighbourhoods at varying scales, which is used as a characteristic feature in determining the spatial regularity of elementary texture structures. The experimental results on a variety of texture images demonstrate the efficiency and robustness of the proposed algorithm.