Saliency detection methods which center on RGB images are sensitive to surrounding environments. Fusing complementary RGB and thermal infrared (RGB-T) images is an effective way to promote the final saliency performance. However, there are relatively few datasets and algorithms for RGB-T saliency detection, which is the prominent problem in this research field. Therefore, an unsupervised method that does not require a large amount of labeled data is proposed for RGB-T image saliency detection in this paper. At first, we construct the graph model in which the superpixels are treated as graph nodes. Instead of utilizing the Euclidean distance to construct initial affinity matrix, a novel node classification distance is designed to explore the local relationship and graph geometrical structure of nodes. Additionally, the advantageous constraint is proposed to increase the sparsity of the image, which not only makes the initial affinity matrix sparse and accurate but also enhances the foreground or background consistency during the graph learning. Furthermore, an adaptive ranking algorithm fusing classification distance and sparse constraint is used to unify the graph affinity learning and the computation of saliency values, which helps to generate more accurate saliency results. Experiments on two public RGB-T datasets demonstrate that the applied method performs desirably against the state-of-the-art algorithms.

以RGB图像为中心的显着性检测方法对周围环境敏感。融合互补的RGB和热红外（RGB-T）图像是提高最终显着性的有效方法。然而，用于RGB-T显着性检测的数据集和算法相对较少，这是该研究领域中的突出问题。因此，本文提出了一种不需要大量标记数据的无监督方法来进行RGB-T图像显着性检测。首先，我们构建将超像素视为图节点的图模型。代替利用欧几里得距离来构造初始亲和力矩阵，设计了一种新颖的节点分类距离来探索节点的局部关系和图形几何结构。此外，提出了有利的约束条件来增加图像的稀疏度，这不仅使初始亲和度矩阵稀疏且准确，而且增强了图学习过程中的前景或背景一致性。此外，融合分类距离和稀疏约束的自适应排序算法被用于统一图亲和度学习和显着性值的计算，这有助于产生更准确的显着性结果。在两个公开的RGB-T数据集上进行的实验表明，所应用的方法与最先进的算法相比具有理想的性能。结合分类距离和稀疏约束的自适应排序算法，用于统一图亲和度学习和显着性值的计算，有助于生成更准确的显着性结果。在两个公开的RGB-T数据集上进行的实验表明，所应用的方法与最先进的算法相比具有理想的性能。融合分类距离和稀疏约束的自适应排序算法，用于统一图亲和度学习和显着性值的计算，有助于生成更准确的显着性结果。在两个公开的RGB-T数据集上进行的实验表明，所应用的方法与最先进的算法相比具有理想的性能。