Title: Exploiting Color Name Space for Salient Object Detection,
Abstract: 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.