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

A variational multi-view learning framework and its application to image segmentation,10.1109/ICME.2009.5202792,Zhenglong Li,Qingshan Liu,Hanqing Lu

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