A unified contour-pixel model for figure-ground segmentation

Authors:
Ben Packer;Stephen Gould;Daphne Koller
Affiliations:
Computer Science Department, Stanford University, CA;Electric Engineering Department, Stanford University, CA;Computer Science Department, Stanford University, CA
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Year:
2010

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Abstract

The goal of this paper is to provide an accurate pixel-level segmentation of a deformable foreground object in an image. We combine state-of-the-art local image segmentation techniques with a global object-specific contour model to form a coherent energy function over the outline of the object and the pixels inside it. The energy function includes terms from a variant of the TextonBoost method, which labels each pixel as either foreground or background. It also includes terms over landmark points from a LOOPS model [1], which combines global object shape with landmark-specific detectors. We allow the pixel-level segmentation and object outline to inform each other through energy potentials so that they form a coherent object segmentation with globally consistent shape and appearance. We introduce an inference method to optimize this energy that proposes moves within the complex energy space based on multiple initial oversegmentations of the entire image. We show that this method achieves state-of-the-art results in precisely segmenting articulated objects in cluttered natural scenes.