Selective focus saliency model driven by object class-awareness

Current many salient object detection (SOD) models only focus on highlighting visual conspicuous region but fail to make saliency detection for specific targets. In this paper, a selective focus saliency model driven by object class-awareness (SF-OCA) to run saliency detection is proposed. The framework consists of a visual saliency detection flow, a segmentation-classification flow, and a class-awareness selection module. It combines bottom-up visual perception with a top-down task-driven manner, which is capable of detecting specific category salient targets and eliminating the interference from other saliency areas, providing a new idea for saliency detection. Experimental results show that the method achieves comparable performance with state-of-the-art models on four public saliency datasets. In addition, a new dataset was also built to test the proposed framework for the selective focus saliency detection. Compared with other SOD methods, the method not only highlights visual saliency regions but can choose more important or more noteworthy targets in a class-awareness manner. The method also shows better robustness under a variety of conditions including multi-targets, small targets and complex background.