期刊论文

Zeng, Yu

Lu, HC (reprint author), Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China.

Feng, Mengyang,Lu, Huchuan,Yang, Gang,Borji, Ali

2018-09-01

IEEE TRANSACTIONS ON IMAGE PROCESSING

SCIE

J

27

9

4545-4554

1057-7149

Saliency; salient object detection; visual attention

We propose a novel unsupervised game-theoretic salient object detection algorithm that does not require labeled training data. First, saliency detection problem is formulated as a non-cooperative game, hereinafter referred to as Saliency Game, in which image regions are players who choose to be "background" or "foreground" as their pure strategies. A payoff function is constructed by exploiting multiple cues and combining complementary features. Saliency maps are generated according to each region's strategy in the Nash equilibrium of the proposed Saliency Game. Second, we explore the complementary relationship between color and deep features and propose an iterative random walk algorithm to combine saliency maps produced by the Saliency Game using different features. Iterative random walk allows sharing information across feature spaces, and detecting objects that are otherwise very hard to detect. Extensive experiments over six challenging data sets demonstrate the superiority of our proposed unsupervised algorithm compared with several state-of-the-art supervised algorithms.