IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS

In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.