Hierarchical saliency optimization

Abstract

A variety of methods have been proposed for object level saliency detection, which is useful for many content-based computer vision applications. Unlike most previous work that integrate multiple low level cues to compute the saliency map, this paper presents a novel hierarchical optimization model. First, we compute a rough saliency map using HS method, and then, boundary and foreground seeds are extracted from it, which guide the computation of the background and foreground saliency maps, respectively. Next, a combination of the two saliency maps is performed. In the end, Cellular Automata is applied to optimize it and a threshold method is taken to make the optimized saliency map closer to the ground truth. Experiments on three large datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods in terms of F-measures and MAEs.