RGB-T Saliency Detection via Low-Rank Tensor Learning and Unified Collaborative Ranking

Abstract

Saliency detection is a significant research topic in the field of image processing and computer vision. Currently, most saliency detection methods are applied to RGB images, so that they may encounter adverse scenarios characterized by complex background, inclement weather, and low illumination. Fusing complementary advantages of RGB and thermal infrared (RGB-T) images can effectively boost saliency detection performance. Therefore, we propose a novel RGB-T saliency detection method in this letter. To this end, we first regard superpixels as graph nodes and calculate the affinity matrix for each feature. Then, we propose a low-rank tensor learning model for the graph affinity, which can suppress redundant information and improve the relevance of similar image regions. Finally, a novel ranking algorithm is proposed to jointly obtain the optimal affinity matrix and saliency values under a unified structure. Test results on two RGB-T datasets illustrate the proposed method performs well when against the state-of-the-art algorithms.