Evaluating group-based relevance feedback for content-based image retrieval

Munehiro Nakazato, Charlie Dagli, Thomas S Huang

Research output: Contribution to conferencePaper

Abstract

We have been developing new relevance feedback algorithms for Content-based Image Retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new user interface, called group-oriented user interface, the user's interest can be expressed with multiple groups of positive and negative image examples. This provides users with greater flexibility as compared with previous systems that consider image query as one or two-class problems. In this paper, we analyze our new algorithm qualitatively and quantitatively. For comparison with previous approaches, the systems are tested on both toy problems and real image retrieval tasks. From the results of our experiments, we suggest when and how our algorithm has advantages over the previous methods.

Original languageEnglish (US)
Pages599-602
Number of pages4
StatePublished - Dec 17 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: Sep 14 2003Sep 17 2003

Other

OtherProceedings: 2003 International Conference on Image Processing, ICIP-2003
CountrySpain
CityBarcelona
Period9/14/039/17/03

ASJC Scopus subject areas

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

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  • Cite this

    Nakazato, M., Dagli, C., & Huang, T. S. (2003). Evaluating group-based relevance feedback for content-based image retrieval. 599-602. Paper presented at Proceedings: 2003 International Conference on Image Processing, ICIP-2003, Barcelona, Spain.