  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 6 challenging datasets demonstrate the superiority
of our proposed unsupervised algorithm compared to several state of the art
supervised algorithms.
