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

Author

Duong, Thach-Thao ; Lim, Joo-Hwee ; Vu, Hai-Quan ; Chevallet, Jean-Pierre

Author_Institution

Fac. of Inf. Technol., Ho Chi Minh Univ. of Sci., Ho Chi Minh City

fYear

2008

fDate

13-17 July 2008

Firstpage

306

Lastpage

310

Abstract

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.

Keywords

feature extraction; image classification; image recognition; trees (mathematics); unsupervised learning; hierarchical feature tree distribution; image classification; image recognition; unsupervised learning; Cities and towns; Classification tree analysis; Computer vision; Feature extraction; Image classification; Image representation; Image segmentation; Image storage; Information technology; Unsupervised learning; distribution; hierarchical tree; image classification; unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Research, Innovation and Vision for the Future, 2008. RIVF 2008. IEEE International Conference on

Conference_Location

Ho Chi Minh City

Print_ISBN

978-1-4244-2379-8

Electronic_ISBN

978-1-4244-2380-4

Type

conf

DOI

10.1109/RIVF.2008.4586371

Filename

4586371