RGBT Salient Object Detection: Benchmark and A Novel Cooperative Ranking Approach

Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal infrared (RGBT), may be an effective way for boosting saliency detection performance. This work contributes a RGBT image dataset, which includes 821 spatially aligned RGBT image pairs and their ground truth annotations for saliency detection purpose. Moreover, 11 challenges are annotated on these image pairs for performing the challenge-sensitive analysis and 3 kinds of baseline methods are implemented to provide a comprehensive comparison platform. With this benchmark, we propose a novel approach based on a cooperative ranking algorithm for RGBT saliency detection. In particular, we introduce a weight for each modality to describe the reliability and a ℓ 1 -based cross-modal consistency in a unified ranking model, and design an efficient solver to iteratively optimize several subproblems with closed-form solutions. Extensive experiments against baseline methods demonstrate the effectiveness of the proposed approach on both our introduced dataset and a public dataset.