A multi-class relevance feedback approach to image retrieval

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


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
Number of pages4
StatePublished - 2001
EventIEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece
Duration: 7 Oct 200110 Oct 2001


OtherIEEE International Conference on Image Processing (ICIP) 2001


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