WISECODE: wise image segmentation based on community detection

Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the edges. Afterwards, a community detection algorithm is applied, and communities are extracted such that the highest modularity measure is achieved. Finally, a post-processing algorithm merges very small regions with the greater ones, further enhancing the final result. One of the most striking features of the proposed method, is the ability to segment the input image without the need to specify a predefined number of segments manually. This remarkable feature results from the optimal modularity value, which is utilised by this method. It is also able to segment the input image into a user defined number of segments. Extensive experiments have been performed, and the results show that the proposed scheme can reliably segment the input colour image into good subjective criteria.