A Hybrid Trademark Retrieval System Using Four-Gray-Level Zernike Moments and Image Compactness Indices

There are just too many trademarks out there so that a good automated retrieval system is required to help to protect them from possible infringements. However, it is from people, i.e., the general consumers' viewpoint how similar or confusing two trademarks can be. Thus, in this paper we propose a hybrid system where we patently incorporate human inputs into a computerized trademark retrieval scheme. Various surveys involving general consumers' cognition and responses are conducted, and the results are used as benchmarks in developing the automated part. The core mathematical features used in the scheme are four-gray-level Zernike moments and two new image compactness indices. Experimental results show that this hybrid system, when compared with human-generated results, is able to achieve an average accuracy rate of 95% while that of the closest competing existing method is 65%.