Description

This paper proposes an unsupervised color–texture image segmentation method. In order to enhance the effects of segmentation, a new color–texture descriptor is designed by integrating the compact multi-scale structure tensor (MSST), total variation (TV) flow, and the color information. Due to the fact that MSST does not work well for separating regions with large-scale texture, the total variation flow is used to auxiliary describe the texture feature by extracting local scale information. To segment the color–texture image in an unsupervised and multi-label way, the multivariate mixed student’s t-distribution (MMST) is chosen for probability distribution modeling, as MMST can describe the distribution of color–texture features accurately. Since the valid class number is hard to adaptively determine in advance, a component-wise expectation–maximization for MMST (CEM3ST) algorithm is proposed, which can effectively initialize the valid class number. Then, we can build up the energy functional according to the valid class number, and