关键词: RGB-D协同显著检测, 目标检索, 协同传播

Abstract:

As background area is similar to foreground region in RGB-D co-saliency detection algorithm and is easy to be classified as saliency region, an RGB-D co-saliency detection algorithm is proposed to select more accuracy seeds and reduce misclassification rate. Original image, depth image, and initial saliency map are input, which is obtained from existing algorithm, and Depth Shape Prior（DSP） is used to combine depth image and initial saliency map adaptively to generate a better initial saliency map. Objectness estimation is applied to pick out superpixels, which has higher possibility to be saliency region. Co-saliency propagation algorithm and cellular automaton optimization are utilized to get a more accuracy saliency map. Experimental results on RGBD Cosal150 dataset demonstrate the effectiveness and superiority of the proposed algorithm.

Key words: RGB-D co-saliency detection, objectness estimation, co-saliency propagation