Texture image segmentation based on entropy theory

A two-stage segmentation algorithm for textured object image is proposed based on variational approaches and entropy theory. Structure tensor of the image is adopted as the texture feature for the discrimination of texture patterns. In order to make the four information channels cooperatively push the evolving contour towards the textured foreground object, a pre-processing based on mean shift algorithm is applied, which can implement edge-preserving smoothing and has a definite stopping criterion. The Dream/sup 2/s framework is extended to the structure tensor data in the first segmentation stage in which the differential entropy is adopted as regional descriptors, and Gaussian distribution for both the foreground part and the background part is assumed. In the second stage, the nonparametric density distributions are exploited, and the mutual information between the intensity random variables of different information channels and the binary label random variable is adopted as the criterion to minimize. The nonparametric representation can describe the truly nongaussianity of data, and thus can refine the initial segmentation result obtained from the first stage.