Image co-segmentation using maximum common subgraph matching and region co-growing

Abstract

We propose a computationally efficient graph based image co-segmentation algorithm where we extract objects with similar features from an image pair or a set of images. First we build a region adjacency graph (RAG) for each image by representing image superpixels as nodes. Then we compute the maximum common subgraph (MCS) between the RAGs using the minimum vertex cover of a product graph obtained from the RAG. Next using MCS outputs as the seeds, we iteratively co-grow the matched regions obtained from the MCS in each of the constituent images by using a weighted measure of inter-image feature similarities among the already matched regions and their neighbors that have not been matched yet. Upon convergence, we obtain the co-segmented objects. The MCS based algorithm allows multiple, similar objects to be co-segmented and the region co-growing stage helps to extract different sized, similar objects. Superiority of the proposed method is demonstrated by processing images containing different sized objects and multiple objects.