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

Paper


Duong, Thach-Thao, Lim, Joo-Hwee, Vu, Hai-Quan and Chevallet, Jean-Pierre. 2008. "Unsupervised Learning for Image Classification based on Distribution of Hierarchical Feature Tree." 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies (RIVF 2008). Ho Chi Minh, Vietnam 13 - 17 Jul 2008 United States. https://doi.org/10.1109/RIVF.2008.4586371
Paper/Presentation Title

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

Presentation TypePaper
AuthorsDuong, Thach-Thao (Author), Lim, Joo-Hwee (Author), Vu, Hai-Quan (Author) and Chevallet, Jean-Pierre (Author)
Journal or Proceedings TitleProceedings of the 2008 IEEE International Conference on Research, Innovation and Vision for the Future (RIVF 2008)
Number of Pages5
Year2008
Place of PublicationUnited States
ISBN9781424423798
Digital Object Identifier (DOI)https://doi.org/10.1109/RIVF.2008.4586371
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/4586371
Conference/Event2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies (RIVF 2008)
Event Details
2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies (RIVF 2008)
Event Date
13 to end of 17 Jul 2008
Event Location
Ho Chi Minh, Vietnam
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.

KeywordsDistribution; Hierarchical tree; Image classification; Unsupervised learning
ANZSRC Field of Research 2020460304. Computer vision
Byline AffiliationsVietnam National University, Vietnam
Institute for Infocomm Research, Singapore
Institution of OriginUniversity of Southern Queensland
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https://research.usq.edu.au/item/q7125/unsupervised-learning-for-image-classification-based-on-distribution-of-hierarchical-feature-tree

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