# RGB-T Image Saliency Detection via Collaborative Graph Learning

## TL;DR

This paper introduces a novel collaborative graph learning method for RGB-T image saliency detection, leveraging hierarchical deep features and superpixels, and provides a new challenging dataset for the task.

## Contribution

It proposes a new collaborative graph learning algorithm for RGB-T saliency detection and introduces a new dataset with 1000 annotated RGB-T image pairs.

## Key findings

- Outperforms state-of-the-art methods on public and new datasets.
- Effectively fuses RGB and thermal data for improved saliency detection.
- Provides a new challenging dataset for future research.

## Abstract

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.