Unsupervised Learning for Image Classification based on Distribution of Hierarchical Feature Tree

Duong, Thach-Thao ORCID: https://orcid.org/0000-0003-2294-3619 and Lim, Joo-Hwee and Vu, Hai-Quan and Chevallet, Jean-Pierre (2008) Unsupervised Learning for Image Classification based on Distribution of Hierarchical Feature Tree. In: 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies (RIVF 2008), 13 July - 17 July 2008, Ho Chi Minh, Vietnam.


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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 30 Mar 2022 23:01
Last Modified: 30 Mar 2022 23:41
Uncontrolled Keywords: Distribution; Hierarchical tree; Image classification; Unsupervised learning
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision
Identification Number or DOI: https://doi.org/10.1109/RIVF.2008.4586371
URI: http://eprints.usq.edu.au/id/eprint/46983

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