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Understanding Popout through Repulsion
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Stella X. Yu and Jianbo Shi
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IEEE Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii, USA, 9-15 Dec 2001
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Paper
(w/ correction)
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Slides
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Abstract
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We consider the problem of segmenting an image into foreground and background, with foreground containing solely objects of interest known a priori. We propose an integration model that incorporates both edge detection and object part detection results. It consists of two parallel processes: low-level pixel grouping and high-level patch grouping. We seek a solution that optimizes a joint grouping criterion in a reduced space enforced by grouping correspondence between pixels and patches. Using spectral graph partitioning, we show that a near global optimum can be found by solving a constrained eigenvalue problem. We report promising experimental results on a dataset of $15$ objects under clutter and occlusion.
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Keywords
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image segmentation, figure-ground, object recognition, graph partitioning, constrained optimization
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Note
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with correction of bounds for infinite regularization: Tab. 2 and Fig. 4
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