Image categorization by learning with context and consistency

This paper presents a novel semi-supervised learning method which can make use of intra-image semantic context and inter-image cluster consistency for image categorization with less labeled data. The image representation is first formed with the visual keywords generated by clustering all the blocks that we divide images into. The 2D spatial Markov chain model is then proposed to capture the semantic context across these keywords within an image. To develop a graph-based semi-supervised learning approach to image categorization, we incorporate the intra-image semantic context into a kind of spatial Markov kernel which can be used as the affinity matrix of a graph. Instead of constructing a complete graph, we resort to a k-nearest neighbor graph for label propagation with cluster consistency. To the best of our knowledge, this is the first application of kernel methods and 2D Markov models simultaneously to image categorization. Experiments on the Corel and histological image databases demonstrate that the proposed method can achieve superior results.

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