Image segmentation using local spectral histograms

We propose a new algorithm for image segmentation. We use the spectral histogram, which is a vector consisting of marginal distributions of responses from chosen filters as a generic feature for texture as well as intensity images. Motivated by a new segmentation energy functional, we derive an iterative and deterministic approximation algorithm for segmentation. Based on the relationships between different scales and neighboring windows, we also develop an algorithm which can automatically detect homogeneous regions in an input image, which may consist of texture regions. To reduce the boundary uncertainty due to the large spatial window used for spectral histograms, we propose a novel local feature by building precise probability models based on current segmentation results. We have applied our algorithm to intensity, texture, and natural images and obtained good results with accurate texture boundaries.