Content-based similarity retrieval of trademarks using relevance feedback

This paper addresses the problem of how to efficiently and effectively retr ieve images similar to a query from a trademark database purely on the basi s of low-level feature analysis. It investigates the hypothesis that the lo w-level image features used to index the trademark images can be correlated with image contents by applying a relevance feedback mechanism that evalua tes the feature distributions of the images the user has judged relevant, o r not relevant and dynamically updates both the similarity measure and quer y in order to better represent the user's particular information needs. Exp erimental results on a database of 1100 trademarks are reported and comment ed. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd . All rights reserved.