Co-Saliency Detection Using Collaborative Feature Extraction And High-To-Low Feature Integration

Co-saliency detection, as a developing research branch of saliency detection, devotes to identify the common salient objects in a group of related images. The major challenge of co-saliency detection is how to effectively represent features considering both intra-image and inter-image information. In this paper, we propose a co-saliency detection model using collaborative feature extraction and high-to-low feature integration. We first feed the target image and its co-images into the Individual Feature Extraction Module (IFEM) to produce multi-level individual features. Then, to capture the collaborative inter-image information, the Collaborative Feature Extraction Module (CFEM) is applied to all highest-level individual features, generating the collaborative feature. Finally, we build a High-to-low Feature Integration Module (HFIM), which integrates the collaborative feature and multi-level individual features of the target image, to enrich the collaborative feature with individual intra-image information. Extensive experiments on two public datasets demonstrate that the proposed model achieves the state-of-the-art performance.