A hierarchical graph model for object cosegmentation

Given a set of images containing similar objects, cosegmentation is a task of jointly segmenting the objects from the set of images, which has received increasing interests recently. To solve this problem, we present a novel method based on a hierarchical graph. The vertices of the hierarchical graph involve pixels, superpixels and heat sources, and cosegmentation is performed as iterative object refinement in the three levels. With the inter-image connection in the heat source level and the intra-image connection in the superpixel level, we progressively update the object likelihoods by transferring message across images via belief propagation, diffusing heat energy within individual image via random walks, and refining the foreground objects in the pixel level via guided filtering. Besides, a histogram based saliency detection scheme is employed for initialization. We demonstrate experimental evaluations with state-of-the-art methods over several public datasets. The results verify that our method achieves better segmentation quality as well as higher efficiency.