Effective image retrieval using dominant color descriptor and fuzzy support vector machine

  • Authors:
  • Rui Min;H. D. Cheng

  • Affiliations:
  • Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA;Department of Computer Science, Utah State University, Logan, UT 84322-4205, USA

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

Image retrieval is an active research area in image processing, pattern recognition, and computer vision. Relevance feedback has been widely accepted in the field of content-based image retrieval (CBIR) as a method to boost the retrieval performance. Recently, many researchers have employed support vector machines (SVMs) for relevance feedback. This paper presents a fuzzy support vector machine (FSVM) that is more robust to the four major problems encountered by the conventional SVMs: small size of samples, biased hyperplane, over-fitting, and real-time. To improve the performance, a dominant color descriptor (DCD) is also proposed. Experimental results based on a set of Corel images demonstrate that the proposed system performs much better than the previous methods. It achieves high accuracy and reduces the processing time greatly.