CDSEG: Community detection for extracting dominant segments in color images

Segmentation plays an important role in the machine vision field. Extraction of dominant segments with large number of pixels is essential for some applications such as object detection. In this paper, a new approach is proposed for color image segmentation which uses ideas behind the social science and complex networks to find dominant segments. At first, we extract the color and texture information for each pixel of input image. A network that consists of some nodes and edges is constructed based on the extracted information. The idea of community detection in social networks is used to partition a color image into disjoint segments. Community detection means partitioning vertices of a network into different non-overlapped groups (communities) such that the density of intra-group edges is much higher than the density of intergroup edges. There is a very close relation between communities in the social network and segments in an image. Our results show that community detection approaches will improve the segmentation output compare to other methods available in the literature.