Segmentation by transduction

This paper addresses the problem of segmenting an image into regions consistent with user-supplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold learning tool that is based on the estimation of variants of the Laplace-Beltrami operator and is tightly related to diffusion processes. Segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients for seed pixels. The proposed algorithm essentially relies on a high margin assumption in the space of pixel characteristics. It is simple, fast, and accurate, as demonstrated by qualitative results on natural images and a quantitative comparison with state-of-the-art methods on the Microsoft GrabCut segmentation database.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Florent Ségonne, Jean Ponce, Jean-Yves Audibert, Olivier Duchenne, Renaud Keriven
• Keywords: Broad Brush Strokes, Computer Vision, CVPR 2008, Microsoft Grabcut Segmentation, Seed Pixels, Statistical Transductive Inference, Unclassified Pixels