Salient object detection with fixation priori

Abstract

There are two main areas in saliency detection: fixation prediction and salient object detection. Researchers always use bottom-up low-level features and straightforward rules for salient object detection. However these rules are unreliable in common scenes. As a result, most salient object detection technologies can gain a fine performance in limited situation. While in a more complex scene (such as PASCAL-S dataset), they may miss the really wanted salient objects. Fixation prediction is a technology of predicting the regions which can attract human's attention. Most fixation prediction technologies are based on the discoveries of neuroscience and cognitive psychology. Their rules for fixation prediction more accord with human's sensory mechanism. Thus, fixation map can provide a more reliable salient priori. Based on above reasons, we propose a novel salient object detection method with fixation priori. We refine fixation map to gain a reliable priori, and conduct salient object detection based on the priori. We perform experiment on two datasets, and find that our method outperforms the comparative state-of-art salient object detection technologies.