Abstract

World is emerging with trade practices in the global scenario moving mainly towards the brands both for the products and service equally. Most of the Companies are trying to establish the brand name in the market progressively for the global recognition. As a result, Trademarks play today a very critical and important role. Every Company or Financial organization wants a distinguished trademark for brand name and market viability. Trademark registration and its evaluation for distinctiveness is thus becoming very tedious job for registration offices. Millions of trademarks already registered and millions of applications filed for trademarks registration are aggravating the problem of issuing the trademark certificates. There are different techniques and approaches currently in use for distinctness check for trademarks. The present paper gives the overview of the most popular and appreciated image processing techniques and approaches for the trademark distinctness check. Content Based Image retrieval techniques are widely used for that purpose and some other approaches like shape and texture based similarity finding techniques are also used. In the present paper, our approach is to summarize the most widely used techniques for the trademark distinctness check.

References

• M.J.Swain and D.H.Ballard, “Color Indexing,” International Journal of Computer Vision, vol. 7, no. 1, pp. 11–32, September 1991
• A. K. Jain and A. Vailaya, “Shape-Based Retrieval: A Case Study with Trademark Image Databases,” Pattern Recognition, vol. 31, no. 9, pp. 1369–1390, 1998
• T. Kato, “Database architecture for content based image retrieval”, Proceedings of SPIE Image Storage and Retrieval Systems, Vol, 1662, pp, 112-123, 1992
• J.K. Wu, C.P.Lam, B.M. Mehtre, Y.J. Gao, and A. Narasimhalu, “Content based retrieval for trademark registration”, Multimedia Tools Application, vol. 3, no. 3, pp. 245-267, 1996
• S. Alwis and J. Austin, “A Novel architecture for trademark image retrieval system”, Proceedings of the Challenge of Image Retrieval, British Computer Society, UK, 1998.
• J. P. Eakins, M.E. Graham, and J.M. Boardman, “Evaluation of a trademark image retrieval system”, Information Retrieval Research, the 19th Annual BCS-IRSG Colloquium on IR Research, 1997
• Chia-Hung Wei, Yue Li, Wing Yin Chau, and Chang-Tsun Li, Trademark Image Retrieval Using Synthetic Features for Describing Global Shape and Interior Structure, Elsevier Science Inc, Pattern Recognition, Vol 42, Issue 3 March 2009, pages 386-394
• M. Hussain and J.P. Eakins, “Component-based visual clustering using the self-organizing map,” Neural Networks, vol. 20, no. 2, pp. 260-273, 2007.
• A. Cerri, M. Ferri and D. Giorgi, “Retrieval of trademark images by means of size functions,”Graphical Models, vol. 68, no. 5-6, pp. 451-471, 2006
• H. Jiang, C.-W. Ngo and H.-K. Tan, “Gestalt-based feature similarity measure in trademark database,” Pattern Recognition, vol. 39, no. 5, pp. 988-1001, 2006.
• M.H. Hung, C.H. Hsieh, and C.-M. Kuo, “Similarity retrieval of shape images based on database classification,” Journal of Visual Communication & Image Representation, vol. 17, no. 5, 970-985, 2006
• W.-Y. Kim and Y.-S. Kim, “A region-based shape descriptor using Zernike moments, “Signal Processing: Image Communication, vol. 16, no. 1-2, pp. 95-102, 2000
• Kato T (1992) “Database architecture for content-based image retrieval” in Image Storage and Retrieval Systems (Jambardino, A A and Niblack, W R , eds), Proc SPIE 1662, 112-123
• S. Krishnamachari and R. Chellappa, “Multiresolution Gauss-Markov Random Field Models for Texture Segmentation,” IEEE Trans. Image Processing, vol. 6, no. 2, 1997
• G.L. Gimel'farb and A.K. Jain, “On Retrieving Textured Images from an Image Database,” Pattern Recognition, vol. 29, no. 9, pp. 1,461-1,483, 1996
• Arnold W.M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, Ramesh Jain, Content-Based Image Retrieval at the End of the Early Years, December 2000, IEEE transactions on pattern Analysis and Machine Intelligence, Vol 22, No 12.
• W.Y. Ma and B.S. Manjunath, “NeTra: A Toolbox For Navigating Large Image Databases”, IEEE Int. Conf. on Image Processing, 1997, pp. 568-571
• Flickner, M et al “Query by image and video content: the QBIC system” IEEE Computer1995, 28(9), 23-32
• Pentland, R.W. Picard and S. Sclaroff(1996) “Photobook: Content-Based Manipulation of Image Databases ” International Journal of Computer Vision 18(3), 233-254
• Theo Pavlidis, Limitations of Content-based Image Retrieval, October 24, 2008, Version of June 2008http://theopavlidis.com/technology/CBIR/PaperB/vers3.htm
• Theo Pavlidis, A Set of Images for Testing CBIR Techniques and a Collection of Results, December 2009. http://theopavlidis.com/technology/CBIR/PaperD/testset.htm