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2008

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

8 years 3 months ago
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 feature's 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. Keywords- image classification, hierarchical tree, unsupervised learning, distribution
Thach-Thao Duong, Joo-Hwee Lim, Hai-Quan Vu, Jean-
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where RIVF
Authors Thach-Thao Duong, Joo-Hwee Lim, Hai-Quan Vu, Jean-Pierre Chevallet
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