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ICMCS
2009
IEEE

A variational multi-view learning framework and its application to image segmentation

7 years 12 months ago
A variational multi-view learning framework and its application to image segmentation
The paper presents a novel multi-view learning framework based on variational inference. We formulate the framework as a graph representation in form of graph factorization: the graph comprises of factor graphs, which are used to describe internal states of views. Each view is modeled with a Gaussian mixture model. The proposed framework has three main advantages 1) less constraint assumed on data, 2) effective utilization of unlabeled data, and 3) automatic data structure inferring: proper data structure can be inferred in only one round. The experiments on image segmentation demonstrate its effectiveness.
Zhenglong Li, Qingshan Liu, Hanqing Lu
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICMCS
Authors Zhenglong Li, Qingshan Liu, Hanqing Lu
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