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.
|Number of pages||4|
|State||Published - 1 Jan 2001|
|Event||IEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece|
Duration: 7 Oct 2001 → 10 Oct 2001
|Other||IEEE International Conference on Image Processing (ICIP) 2001|
|Period||7/10/01 → 10/10/01|