Depth information has been shown to affect identification of visually salient regions in images. In this paper, we investigate the role of depth in saliency detection in the presence of (i) competing saliencies due to appearance, (ii) depth-induced blur and (iii) centre-bias. Having established through experiments that depth continues to be a significant contributor to saliency in the presence of these cues, we propose a 3D-saliency formulation that takes into account structural features of objects in an indoor setting to identify regions at salient depth levels. Computed 3D saliency is used in conjunction with 2D saliency models through non-linear regression using SVM to improve saliency maps. Experiments on benchmark datasets containing depth information show that the proposed fusion of 3D saliency with 2D saliency models results in an average improvement in ROC scores of about 9% over state-of-the-art 2D saliency models.
This finding was also supported in REF , where improvements of 3D saliency models are achieved compared with 2D saliency models specifically taking account of objects in an indoor setting.
Depth really Matters: Improving Visual Salient Region Detection with Depth.
BMVC
Computer Science