A feature re-weighting approach for relevance feedback in image retrieval

Yimin Wu, Aidong Zhang
Proceedings. International Conference on Image Processing  
Users of image databases often prefer to retrieve relevant images by categories. Unfortunately, images are usually indexed by low-level features like color, texture and shape, which often fail to capture high-level concepts well. To address this issue, relevance feedback has been extensively used to associate low-level image features with highlevel concepts. Among all existing relevance feedback approaches, query movement and feature re-weighting have been proven to be suitable for large-scaled
more » ... image databases with high dimensional image features. In this paper, we present a feature re-weighting approach using relevant images as well as irrelevant ones in the relevance feedback. As far as feature re-weighting approaches are concerned, one of their common drawbacks is that the feature re-weighting process is prone to be trapped by suboptimal states. To overcome this problem, we introduce a disturbing factor, which is based on the Fisher criterion, to push the feature weights out of sub-optimum. Experimental results on a large-scaled image database with 31,438 COREL images demonstrate the effectiveness of the presented method.
doi:10.1109/icip.2002.1040017 dblp:conf/icip/WuZ02 fatcat:3lsa7nkugzbc5lygl2tnqhn7si