Single Image Segmentation with Estimated Depth


A novel framework for automatic object segmentation is proposed that exploits depth information estimated from a single image as an additional cue. For example, suppose that we have an image containing an object and a background with a similar color or tex- ture to the object. The proposed framework enables us to automatically extract the object from the image while eliminating the misleading background. Although our segmenta- tion framework takes a form of a traditional formulation based on Markov random fields, the proposed method provides a novel scheme to integrate depth and color information, which derives objectness/backgroundness likelihood. We also employ depth estimation via supervised learning so that the proposed method can work even if it has only a single input image with no actual depth information. Experimental results with a dataset origi- nally collected for the evaluation demonstrate the effectiveness of the proposed method against the baseline method and several existing methods for salient region detection.

British Machine Vision Conference (BMVC)