Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps

Wai Tak Wong, Frank Y. Shih, Jung Liu

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

Image retrieval based on image content has become an important topic in the fields of image processing and computer vision. In this paper, we present a new method of shape-based image retrieval using support vector machines (SVM), Fourier descriptors and self-organizing maps. A list of predicted classes for an input shape is obtained using the SVM, ranked according to their estimated likelihood. The best match of the image to the top-ranked class is then chosen by the minimum mean square error. The nearest neighbors can be retrieved from the self-organizing map of the class. We employ three databases of 99, 216, and 1045 shapes for our experiment, and obtain prediction accuracy of 90%, 96.7%, and 84.2%, respectively. Our method outperforms some existing shape-based methods in terms of speed and accuracy.

Original languageEnglish (US)
Pages (from-to)1878-1891
Number of pages14
JournalInformation sciences
Volume177
Issue number8
DOIs
StatePublished - Apr 15 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Keywords

  • Fourier descriptor
  • Image retrieval
  • Object recognition
  • Self-organizing map
  • Support vector machine

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