Content-based similarity retrieval of trademarks using relevance feedback

Abstract

This paper addresses the problem of how to efficiently and effectively retrieve images similar to a query from a trademark database purely on the basis of low-level feature analysis. It investigates the hypothesis that the low-level image features used to index the trademark images can be correlated with image contents by applying a relevance feedback mechanism that evaluates the feature distributions of the images the user has judged relevant, or not relevant and dynamically updates both the similarity measure and query in order to better represent the user's particular information needs. Experimental results on a database of 1100 trademarks are reported and commented.