A Novel Fuzzy Visual Object Classification Approach

Altintakan U. L., YAZICI A., KOYUNCU M.

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Brisbane, Australia, 10 - 15 June 2012 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Brisbane
  • Country: Australia
  • Middle East Technical University Affiliated: Yes


Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM.