Segmentation given partial grouping constraints

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stella X Yu, Jianbo Shi

Abstract

We consider data clustering problems where partial grouping is known a priori. We formulate such biased grouping problems as a constrained optimization problem, where structural properties of the data define the goodness of a grouping and partial grouping cues define the feasibility of a grouping. We enforce grouping smoothness and fairness on labeled data points so that sparse partial grouping information can be effectively propagated to the unlabeled data. Considering the normalized cuts criterion in particular, our formulation leads to a constrained eigenvalue problem. By generalizing the Rayleigh-Ritz theorem to projected matrices, we find the global optimum in the relaxed continuous domain by eigendecomposition, from which a near-global optimum to the discrete labeling problem can be obtained effectively. We apply our method to real image segmentation problems, where partial grouping priors can often be derived based on a crude spatial attentional map that binds places with common salient features or focuses on expected object locations. We demonstrate not only that it is possible to integrate both image structures and priors in a single grouping process, but also that objects can be segregated from the background without ...Continue Reading

References

Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman

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Citations

Sep 15, 2005·Neural Computation·Qi Zhao, David J Miller
Jul 5, 2012·Medical Physics·Liang GaoWufan Chen
Aug 16, 2011·Medical Image Analysis·Elena Bernardis, Stella X Yu
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Jul 23, 2014·IEEE Transactions on Neural Networks and Learning Systems·Jayaram RaghuramGeorge Kesidis
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Jan 27, 2015·IEEE Transactions on Neural Networks and Learning Systems·Xiatian ZhuShaogang Gong
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Mar 16, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Peng WangPhilip H S Torr
Feb 26, 2016·IEEE Transactions on Neural Networks and Learning Systems·Pengjiang QianRaymond F Muzic

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