Classify by Representative or Associations (CBROA): A hybrid approach for image classification

S. Tseng, Chon Jei Lee, Ja Hwung Su

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

9 Scopus citations

Abstract

Image classification has been an interesting research issue in multimedia content analysis due to the wide applications. In this paper, we observe that images can be classified (or annotated) in two ways: i) Classify by some main object, ii) Classify by multiple objects with their relations. These two types of images usually exist concurrently in real-life image databases. Although a number of image classification methods have been propose, they can only handle one certain type of images well and fail to deal with both types of images correctly at the same time. In this paper, we propose a hybrid image classification method, namely "CBROA" (Classify By Representative Or Associations), that can effectively classify both types of images at the same time. CBROA integrates the decision tree and association rules mining method in an adaptive manner with construction of a virtual semantic ontology. Experimental results show that CBROA outperforms other classification methods in terms of classification accuracy in classifying mixed types of images.

Original languageEnglish
Title of host publicationProceedings of the 6th International Workshop on Multimedia Data Mining, MDM '05
Subtitle of host publicationMining Integrated Media and Complex Data
Pages61-69
Number of pages9
DOIs
StatePublished - 1 Dec 2005
Event6th International Workshop on Multimedia Data Mining, MDM '05: Mining Integrated Media and Complex Data - Chicago, IL, United States
Duration: 21 Aug 200521 Aug 2005

Publication series

NameProceedings of the 6th International Workshop on Multimedia Data Mining, MDM '05: Mining Integrated Media and Complex Data

Conference

Conference6th International Workshop on Multimedia Data Mining, MDM '05: Mining Integrated Media and Complex Data
CountryUnited States
CityChicago, IL
Period21/08/0521/08/05

Keywords

  • association rules
  • data mining
  • decision tree
  • image classification

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