Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
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In this paper, we proposed a novel approach to shape classification. A new shape tree based on junction nodes can represent the global structure in a simple way. The statistic distribution of junctions can be learned by merging the shape trees. In the process of learning, context of a junction node is obtained to improve the rate of classification. We illustrate the utility of the proposed method on the problem of 2D shape classification using the new shape tree representation.