Unsupervised learning for image classification based on distribution of hierarchical feature tree

The classification image into one of several categories is a problem arisen naturally under a wide range of circumstances. In this paper, we present a novel unsupervised model for the image classification based on featurepsilas distribution of particular patches of images. Our method firstly divides an image into grids and then constructs a hierarchical tree in order to mine the feature information of the image details. According to our definition, the root of the tree contains the global information of the image, and the child nodes contain detail information of image. We observe the distribution of features on the tree to find out which patches are important in term of a particular class. The experiment results show that our performances are competitive with the state of art in image classification in term of recognition rate.

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