Feature filtering in relevance feedback of image retrieval based on a statistical approach

Hong Fu, Zheru Chi, Dagan Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experiment results show that the proposed approach is more efficient and robust than the traditional method.
Original languageEnglish
Title of host publication2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
Pages647-650
Number of pages4
Publication statusPublished - 1 Dec 2004
Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004 - Hong Kong, China, Hong Kong
Duration: 20 Oct 200422 Oct 2004

Conference

Conference2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2004
CountryHong Kong
CityHong Kong, China
Period20/10/0422/10/04

ASJC Scopus subject areas

  • Engineering(all)

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