An Unsupervised Game-Theoretic Approach to Saliency Detection

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 &quo...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-09, Vol.27 (9), p.4545-4554
Main Authors: Yu Zeng, Mengyang Feng, Huchuan Lu, Gang Yang, Borji, Ali
Format: Article
Language:English
Subjects:
Algorithms
Economic models
Extraterrestrial measurements
Feature extraction
Game theory
Games
Image color analysis
Iterative algorithms
Neural networks
Object recognition
Random walk
Salience
Saliency
Saliency detection
salient object detection
visual attention
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2018.2838761