Image retrieval with relevance feedback: From heuristic weight adjustment to optimal learning methods

Thomas S Huang, X. S. Zhou

Research output: Contribution to conferencePaperpeer-review

Abstract

Various relevance feedback algorithms have been proposed in recent years in the area of content-based image retrieval. This paper gives a brief review and analysis on existing techniques-from early heuristic-based feature weighting schemes to recently proposed optimal learning algorithms. In addition, the kernel-based biased discriminant analysis (KBDA) is proposed to fit the unique nature of relevance feedback as a biased classification problem. As a novel variant of traditional discriminant analysis, the proposed algorithm provides a trade-off between discriminant transform and regression. The kernel form is derived to deal with non-linearity in an elegant way. Experimental results indicate that significant improvement in retrieval performance is achieved by the new scheme.

Original languageEnglish (US)
Pages2-5
Number of pages4
StatePublished - Jan 1 2001
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001

Other

OtherIEEE International Conference on Image Processing (ICIP)
CountryGreece
CityThessaloniki
Period10/7/0110/10/01

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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