Similarity retrieval of trademark images

We investigate 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 judged relevant, or not relevant, by the user, 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.