Cue Integration for Figure/Ground Labeling

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Xiaofeng Ren, Jitendra Malik, Charless Fowlkes


We present a model of edge and region grouping using a conditional random field built over a scale-invariant representation of images to integrate multiple cues. Our model includes potentials that capture low-level similarity, mid-level curvilinear continuity and high-level object shape. Maximum likelihood parameters for the model are learned from human labeled groundtruth on a large collection of horse images using belief propagation. Using held out test data, we quantify the information gained by incorporating generic mid-level cues and high-level shape.