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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers
Pages306-310
Number of pages5
ISBN (Print)9781424423798
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies - Ho Chi Minh City, Viet Nam
Duration: 13 Jul 200817 Jul 2008

Publication series

NameRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies

Conference

ConferenceRIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies
Country/TerritoryViet Nam
CityHo Chi Minh City
Period13/07/0817/07/08

Keywords

  • Distribution
  • Hierarchical tree
  • Image classification
  • Unsupervised learning

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