@inproceedings{3c6b2d542b814846b7fce2dedea9372b,
title = "Unsupervised learning for image classification based on distribution of hierarchical feature tree",
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 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 = "Distribution, Hierarchical tree, Image classification, Unsupervised learning",
author = "Duong, {Thach Thao} and Lim, {Joo Hwee} and Vu, {Hai Quan} and Chevallet, {Jean Pierre}",
year = "2008",
doi = "10.1109/RIVF.2008.4586371",
language = "English",
isbn = "9781424423798",
series = "RIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "306--310",
booktitle = "RIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies",
address = "United States",
note = "RIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies ; Conference date: 13-07-2008 Through 17-07-2008",
}