Exploiting Color Name Space for Salient Object Detection

In this paper, we will investigate the contribution of color names for salient object detection. Each input image is first converted to the color name space, which is consisted of 11 probabilistic channels. By exploring the topological structure relationship between the figure and the ground, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only exploiting the surroundedness cue, two global cues with respect to color names are invoked for guiding the computation of another weighted saliency map. Finally, we integrate the two saliency maps into a unified framework to infer the saliency result. In addition, an improved post-processing procedure is introduced to effectively suppress the background while uniformly highlight the salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against 23 saliency models in terms of three evaluation metrics on three public datasets.