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

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 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...

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Yimin Wu, Aidong Zhang
• Keywords: Feature Re-weighting, ICIP 2002, Image Processing, Low-level Image Features, Relevance Feedback