Image segmentation using contour, surface, and depth cues
We target at solving the problem of automatic image segmentation. Although 1D contour and 2D surface cues have been widely utilized in existing work, 3D depth information of an image, a necessary cue according to human visual perception, is however overlooked in automatic image segmentation. In this paper, we study how to fully utilize 1D contour, 2D surface, and 3D depth cues for image segmentation. First, three elementary segmentation modules are developed for these cues respectively. The proposed 3D depth cue is able to segment different textured regions even with similar color, and also merge similar textured areas, which cannot be achieved using state-of-the-art approaches. Then, a content-dependent spectral (CDS) graph is proposed for layered affinity models to produce the final segmentation. CDS is designed to build a more reliable relationship between neighboring surface nodes based on the three elementary cues in the spectral graph. Extensive experiments not only show the superior performance of the proposed algorithm over state-of-the-art approaches, but also verify the necessities of these three cues in image segmentation.