Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely Cross Label Propagation (CLP), is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model.

本文提出了一种基于RGBD图像的共同显著性检测模型，它利用深度信息来增强共显性的识别，并利用多约束特征匹配来计算图像间的约束关系，然后采用Cross Label Propagation（CLP）优化方案在单图和多图间交叉地优化内部和外部显著性地图，从而生成最终的共同显著性结果，实验表明这种基于深度信息和多约束特征匹配的方法可以有效地提高共显性检测的性能。

基于多约束特征匹配和交叉标签传播的RGBD图像共同显著性检测