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In this paper, we propose a novel latent structural model for big data image recognition. It addresses the problem that large amount of labeled training samples are needed in traditional structural models. This method first builds an initial structural model by using only one labeled image. After pooling unlabeled samples into the initial model, an incremental learning process is used to find more...
In this paper, we describe how to build an incremental structured part model for object recognition. The proposed method explores both global structural information and multiple local features of objects for object model characterization. It use part models to represent structure nodes, which encode the local information of an object. The parts are learned through a segmentation and clustering process,...
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