RGBD co-saliency detection via multiple kernel boosting and fusion

RGBD co-saliency detection, which aims at extracting common salient objects from a group of RGBD images with the additional depth information, has become an emerging branch of saliency detection. In this regard, this paper proposes a novel framework via multiple kernel boosting (MKB) and co-saliency quality based fusion. First, on the basis of pre-segmented regions at multiple scales, the regional clustering by feature bagging is exploited to generate the base co-saliency maps. Then the clustering-based samples selection is performed to select the most similar regions with high saliency from different images in the image set. The selected samples are utilized to learn a MKB-based regressor, which is applied to all regions at multiple scales to generate the MKB-based co-saliency maps. Finally, to make full use of both MKB and clustering-based cosaliency maps, a co-saliency quality criterion is proposed for adaptive fusion to generate the final co-saliency maps. Experimental results on a public RGBD co-saliency detection dataset demonstrate that the proposed co-saliency model outperforms the state-of-the-art co-saliency models.