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ICCV
2009
IEEE

FLoSS: Facility Location for Subspace Segmentation

14 years 8 months ago
FLoSS: Facility Location for Subspace Segmentation
Subspace segmentation is the task of segmenting data lying on multiple linear subspaces. Its applications in computer vision include motion segmentation in video, structure-from-motion, and image clustering. In this work, we describe a novel approach for subspace segmentation that uses probabilistic inference via a message-passing algorithm. We cast the subspace segmentation problem as that of choosing the best subset of linear subspaces from a set of candidate subspaces constructed from the data. Under this formulation, subspace segmentation reduces to facility location, a well studied operational research problem. Approximate solutions to this NP-hard optimization problem can be found by performing maximum-a-posteriori (MAP) inference in a probabilistic graphical model. We describe the graphical model and a message-passing inference algorithm. We demonstrate the performance of Facility Location for Subspace Segmentation, or FLoSS, on synthetic data as well as on 3D m...
Nevena Lazic, Inmar Givoni, Brendan Frey
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Nevena Lazic, Inmar Givoni, Brendan Frey
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