Gabor wavelet selection and SVM classification for object recognition

Lin Lin Shen, Zhen Ji

Research output: Journal PublicationArticlepeer-review

44 Citations (Scopus)

Abstract

This paper proposes a Gabor wavelets and support vector machine (SVM)-based framework for object recognition. When discriminative features are extracted at optimized locations using selected Gabor wavelets, classifications are done via SVM. Compared to conventional Gabor feature based object recognition system, the system developed in this paper is both robust and efficient. The proposed framework has been successfully applied to two object recognition applications, i. e., object/non-object classification and face recognition. Experimental results clearly show advantages of the proposed method over other approaches.

Original languageEnglish
Pages (from-to)350-355
Number of pages6
JournalZidonghua Xuebao/Acta Automatica Sinica
Volume35
Issue number4
DOIs
Publication statusPublished - Apr 2009
Externally publishedYes

Keywords

  • Gabor feature
  • Object recognition
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Computer Graphics and Computer-Aided Design

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