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 texture to the object. The proposed framework enables us to automatically extract the object from the image while eliminating the misleading background.
- Although our segmentation 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 originally collected for the evaluation demonstrate the effectiveness of the proposed method against the baseline method and several existing methods for salient region detection.