Building a compact relevant sample coverage for relevance feedback in content-based image retrieval

Abstract

Conventional approaches to relevance feedback in content-based image retrieval are based on the assumption that relevant images are physically close to the query image, or the query regions can be identified by a set of clustering centers. However, semantically related images are often scattered across the visual space. It is not always reliable that the refined query point or the clustering centers are capable of representing a complex query region. In this work, we propose a novel relevance feedback approach which directly aims at extracting a set of samples to represent the query region, regardless of its underlying shape. The sample set extracted by our method is competent as well as compact for subsequent retrieval. Moreover, we integrate feature re-weighting in the process to estimate the importance of each image descriptor. Unlike most existing relevance feedback approaches in which all query points share a same feature weight distribution, our method re-weights the feature importance for each relevant image respectively, so that the representative and discriminative ability for all the images can be maximized. Experimental results on two databases show the effectiveness of our approach. © 2008 Springer Berlin Heidelberg.