Unsupervised Learning for Image Classification based on Distribution of Hierarchical Feature Tree
Paper
| Paper/Presentation Title | Unsupervised Learning for Image Classification based on Distribution of Hierarchical Feature Tree |
|---|---|
| Presentation Type | Paper |
| Authors | Duong, Thach-Thao (Author), Lim, Joo-Hwee (Author), Vu, Hai-Quan (Author) and Chevallet, Jean-Pierre (Author) |
| Journal or Proceedings Title | Proceedings of the 2008 IEEE International Conference on Research, Innovation and Vision for the Future (RIVF 2008) |
| Number of Pages | 5 |
| Year | 2008 |
| Place of Publication | United States |
| ISBN | 9781424423798 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/RIVF.2008.4586371 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/4586371 |
| Conference/Event | 2008 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. |
| Keywords | Distribution; Hierarchical tree; Image classification; Unsupervised learning |
| ANZSRC Field of Research 2020 | 460304. Computer vision |
| Byline Affiliations | Vietnam National University, Vietnam |
| Institute for Infocomm Research, Singapore | |
| Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7125/unsupervised-learning-for-image-classification-based-on-distribution-of-hierarchical-feature-tree
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