Co-Saliency Detection via Hierarchical Consistency Measure

Abstract

Co-saliency detection is a newly emerging research topic in multimedia and computer vision, the goal of which is to extract common salient objects from multiple images. Effectively seeking the global consistency among multiple images is critical to the performance. To achieve the goal, this paper designs a novel model with consideration of a hierarchical consistency measure. Different from most existing co-saliency methods that only exploit common features (such as color and texture), this paper further utilizes the shape of object as another cue to evaluate the consistency among common salient objects. More specifically, for each involved image, an intra-image saliency map is firstly generated via a single image saliency detection algorithm. Having the intra-image map constructed, the consistency metrics at object level and superpixel level are designed to measure the corresponding relationship among multiple images and obtain the inter saliency result by considering multiple visual attention features and multiple constrains. Finally, the intra-image and inter-image saliency maps are fused to produce the final map. Experiments on benchmark datasets are conducted to demonstrate the effectiveness of our method, and reveal its advances over other state-of-the-art alternatives.