Multi-scale Structure Tensor Based Unsupervised Color-Texture Image Segmentation Approach in Multiclass

For semi-supervised and interactive image segmentation methods,they are usually confronted with difficulty with the segmentation task for natural color-texture image automatically.The reason for that is the natural image contained abundant color information and multi-scale texture information,and meanwhile some homogeneous color-texture objects are appeared.An unsupervised color-texture image segmentation approach is proposed in this article.The features of color（vector） and multi-scale texture（set of matrix）are extracted firstly,and then the corresponded energy functions of color and texture are constructed;Meanwhile,a multi-class merging strategy is designed for computing the corresponded merge factor,so that we can integrate the two different kinds of energy functions adaptively.In the following,the optimal solution of merged energy function can be acquired through converting the corresponded energy function as multilayer graph,and then an approximate optimal solution can be calculated in max-flow?min-cut algorithm.Lastly,an adaptive iteration convergence criterion is designed to control the convergence of segmentation process,and meanwhile some experiments are carried out on a large number of synthesis color-texture images andnatural images.The experiments demonstrate the superiority of our proposed method with better region entirety,consistency,and high accuracy.