PROJECT TITLE :

Co-Saliency Detection for RGBD Images Based on Multi-Constraint Feature Matching and Cross Label Propagation - 2018

ABSTRACT:

Co-saliency detection aims at extracting the common salient regions from an image cluster containing two or more relevant images. It is a newly emerging topic in pc vision community. Different from the most existing co-saliency ways focusing on RGB pictures, this Project proposes a unique co-saliency detection model for RGBD pictures, which utilizes the depth info to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the one image saliency model, whereas 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, particularly cross label propagation, is employed to refine the intra and inter saliency maps in a cross way. Finally, all the initial and optimized saliency maps are integrated to get the ultimate co-saliency result. The proposed methodology introduces the depth data 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.