计算机科学
RGB颜色模型
人工智能
图形
基本事实
图像(数学)
模式识别(心理学)
计算机视觉
理论计算机科学
作者
Zhengzheng Tu,Tian Xia,Chenglong Li,Xiaoxiao Wang,Yan Ma,Jin Tang
标识
DOI:10.1109/tmm.2019.2924578
摘要
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.
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