Improving Saliency Detection Via Multiple Kernel Boosting and Adaptive Fusion

Abstract

This letter proposes a novel framework to improve the saliency detection performance of an existing saliency model, which is used to generate the initial saliency map. First, a novel regional descriptor consisting of regional self-information, regional variance, and regional contrast on a number of features with local, global, and border context is proposed to describe the segmented regions at multiple scales. Then, regarding saliency computation as a regression problem, a multiple kernel boosting method based on support vector regression (MKB-SVR) is proposed to generate the complementary saliency map. Finally, an adaptive fusion method via learning a quality prediction model for saliency maps is proposed to effectively fuse the initial saliency map with the complementary saliency map and obtain the final saliency map with improvement on saliency detection performance. Experimental results on two public datasets with the state-of-the-art saliency models validate that the proposed method consistently improves the saliency detection performance of various saliency models.