Bottom-Up Merging Segmentation for Color Images With Complex Areas

Most color images obtained from the real world usually contain complex areas, such as nature scene images, remote sensing images, and medical images. All these type of images are very difficult to be separated accurately and automatically for complex color and structures included. In this paper, we focus on detecting hybrid cues of color image to segment complex scene in a bottom-up framework. The main idea of the proposed segmentation method is based on a two-step procedure: 1) a reasonable superpixels computing method is conducted and 2) a Mumford-Shah (M-S) optimal merging model is proposed for presegment suerpixels. First, a set of seed pixels is positioned at the lowest texture energy map computed from structure tensor diffusion features. Next, we implement a growing procedure to extract superpixels from selected seed pixels with color and texture cues. After that, a color-texture histograms feature is defined to measure similarity between regions, and an M-S optimal merging process is executed by comparing the similarity of adjacent regions with standard deviation constraints to get final segmentation. Extensive experiments are conducted on the Berkeley segmentation database, some remote sensing images, and medical images. The results of experiments have verified that the segmentation effectiveness of the proposed method in segmenting complex scenes and indicated that it is more robust and accurate than conventional methods.