  Image saliency detection is an active research topic in the community of
computer vision and multimedia. Fusing complementary RGB and thermal infrared
data has been proven to be effective for image saliency detection. In this
paper, we propose an effective approach for RGB-T image saliency detection. Our
approach relies on a novel collaborative graph learning algorithm. In
particular, we take superpixels as graph nodes, and collaboratively use
hierarchical deep features to jointly learn graph affinity and node saliency in
a unified optimization framework. Moreover, we contribute a more challenging
dataset for the purpose of RGB-T image saliency detection, which contains 1000
spatially aligned RGB-T image pairs and their ground truth annotations.
Extensive experiments on the public dataset and the newly created dataset
suggest that the proposed approach performs favorably against the
state-of-the-art RGB-T saliency detection methods.
