MSR: A Simple and Effective Metric for Visual Saliency Map Fusion

Various kinds of models have been proposed for saliency detection, but each has its limitations in application. Fusing multiple complementary models is expected to improve the performance. However, most of the fusion methods treat individual model equally and are greatly degraded by poor saliency map. In this paper, we firstly present a no-reference metric to assess the quality of saliency map. Then, a fusion framework is constructed by weighted averaging good saliency maps and filtering out poor ones. In this framework, candidate models can be selected with preference to fast ones. Experimental results on two public datasets show that our method not only outperforms state-of-art unsupervised saliency model but also is more robust than present fusion algorithms.