A multi-class relevance feedback approach to image retrieval

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two-class relevance feedback, relevant and irrelevant. While simple computationally, two-class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. We propose a locally adaptive technique for content-based image retrieval that enables relevance feedback to take on multi-class form. For each given query, we estimate local feature relevance based on Chi-squared analysis using information provided by multi-class relevance feedback. Local feature relevance is then used to compute a flexible metric that is highly adaptive to query locations. As a result, local data distributions can be sufficiently exploited, whereby rapid performance improvement can be achieved. Experimental results using real image data validate the efficacy of our method.

Original languageEnglish
Pages46-49
Number of pages4
StatePublished - 1 Jan 2001
EventIEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece
Duration: 7 Oct 200110 Oct 2001

Other

OtherIEEE International Conference on Image Processing (ICIP) 2001
CountryGreece
CityThessaloniki
Period7/10/0110/10/01

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    Peng, J. (2001). A multi-class relevance feedback approach to image retrieval. 46-49. Paper presented at IEEE International Conference on Image Processing (ICIP) 2001, Thessaloniki, Greece.