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Single image segmentation with estimated depth
  • We incorporate learning-based depth estimation from a single image into our method for object-like region segmentation, which enables us to suppress misleading disctactors and/or backgrounds.

Selected publications

Ryo Yonetani, Akisato Kimura, Hitoshi Sakano, Ken Fukuchi
"Single image segmentation with estimated depth,"
to appear, British Machine Vision Conference (BMVC2012),
Guildford, UK, September 2012.
[ bibliography ]


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.