Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation

TL;DR

This paper introduces a novel RGBD co-saliency detection model that leverages depth information and multi-constraint feature matching, combined with cross label propagation, to improve the identification of common salient regions across image groups.

Contribution

It proposes a new co-saliency detection approach for RGBD images that incorporates depth data and a multi-constraint feature matching scheme with cross label propagation for refinement.

Findings

Effective use of depth information enhances co-saliency detection.

The method outperforms existing RGB co-saliency models on RGBD datasets.

The approach is compatible with various single image saliency models.

Abstract

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.