SALIENT OBJECT DETECTION ON A HIERARCHY OF IMAGE PARTITIONS

This paper presents a region-based model for salient object detection. The model is defined as a combination of saliency maps constructed on a hierarchy of image partitions. The saliency map at each level in the hierarchy takes into account the color similarity between each region and its neighbors and highlights objects at the scale defined by the partition, with accurate boundaries. Experimental results on a publicly available dataset show that the proposed model outperforms many state-of-the-art saliency methods.