Learning Nonlinear Feature Mapping via Constrained Non-Convex Optimization for Unsupervised Salient Object Detection

Mimicking the biological visual attention mechanism to detect salient objects in images has been widely studied in recent years. Most of the existing computational models rely on external learning for saliency prediction, which however lack robustness in diversified scenes. In this paper, an unsupervised learning model is proposed to detect salient objects by fully exploiting the internal information of the scene. Specifically, we formulate saliency detection as a mathematical programming problem with which to learn a nonlinear feature mapping from multi-view features to saliency scores. The optimization objective is to maximize the between-class variance of the attended and background regions in the resulting saliency maps, which is statistically optimal. Moreover, to solve the non-convex constrained mathematical programming problem, a hybrid external point method based particle swarm optimization algorithm is developed to find the optimal solution in a rapid manner. Finally, extensive experiments are conducted on four classical saliency benchmark datasets to test the effectiveness of the proposed method and it shows superior qualitative and quantitative performance than the other 16 state-of-the-art unsupervised saliency models.