Tademark retrieval based on block feature index code

Current trademark classification procedure adopts the Vienna approach developed by World Intellectual Property Organization (WIPO). It has the following drawbacks. 1) Tremendous amount of human efforts are required to classify and annotate each trademark submitted for registration; the time required to complete the registration would increasing as the number of registered trademarks increases. 2) Same trademark may be classified into different classes by two human classifiers. 3) It is difficult to classify trademarks with abstractive contents by human classifier. For the above reasons, it is important to improve the existing trademark classification system by replacing human classifier with effective and precise computer algorithms. As we review previous researches on trademark indexing and retrieval, we find that shape information together with profile and contents of trademark plays an important role in determining the trademark similarity. In this paper, we develop an effective feature for trademark image using block feature index (BFI), the concept is borrowed from vector quantization. For performance evaluation purpose, 3000 trademark images in MEPG-7 were classified into 10 categories for experiments. We compare the recall rate and precision for the proposed BFIC algorithm and two well-known methods: Hu´s 7 moments (H7M) and Zernike moments (ZKM), the proposed BFIC method outperforms the other two methods. The performance can be even better by cascading the proposed BFIC and ZKM.