  In this paper, we will investigate the contribution of color names for the
task of salient object detection. An input image is first converted to color
name space, which is consisted of 11 probabilistic channels. By exploiting a
surroundedness cue, we obtain a saliency map through a linear combination of a
set of sequential attention maps. To overcome the limitation of only using the
surroundedness cue, two global cues with respect to color names are invoked to
guide the computation of a weighted saliency map. Finally, we integrate the
above two saliency maps into a unified framework to generate the final result.
In addition, an improved post-processing procedure is introduced to effectively
suppress image backgrounds while uniformly highlight salient objects.
Experimental results show that the proposed model produces more accurate
saliency maps and performs well against twenty-one saliency models in terms of
three evaluation metrics on three public data sets.
