Content-based trademark retrieval with fuzzy clustering and relevance feedback

Abstract

Since the number of registered trademarks is increasing rapidly, the job of identifying infringement of similar trademarks by human inspection becomes laborious and time consuming. To deal with the problem, we propose an automatic content-based trademark retrieval method. The proposed method automatically selects appropriate features based on feature selection principles to discriminate trademarks. The database trademarks are softly clustered into classes using a fuzzy approach to increase the retrieval speed. The user can submit a query through trademark examples to get a list of database trademarks ordered by similarity ranks. The query results can be iteratively refined by the feedback of interest are retrieved. Experiments are conducted on a trademark database containing 1000 images and the retrieval results are very encouraging.