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Trademark image retrieval using an integrated shape descriptor

Mohd Anuar, Fatahiyah, Setchi, Rossitza and Lai, Yukun 2013. Trademark image retrieval using an integrated shape descriptor. Expert Systems with Applications 40 (1) , pp. 105-121. 10.1016/j.eswa.2012.07.031

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Abstract

Trademarks are distinctive visual symbols with high reputational value, due to the perception of quality and innovation associated with them. They are important reputational assets used as a marketing tool to convey a certain assurance of quality, innovation, and the standards, which the manufacturer seeks to maintain. This motivates the need for trademark protection by providing a solution to prevent infringement. This problem can be addressed by developing retrieval systems capable of comparing the visual similarity of trademarks. This paper contributes to the research in this field by proposing an innovative trademark retrieval technique with improved retrieval performance due to the integration of global and local descriptors. The global descriptor employed is the Zernike moment’s coefficients. The local descriptor is the edge-gradient co-occurrence matrix, derived from the contour information that is considered very important in human perception of visual similarity. The proposed retrieval technique is tested using the standard MPEG-7 shape database of 1400 images and the MPEG-7 trademark database of 3260 images. The results show 5% precision/recall improvement in the case of the MPEG-7 shape database, as well as 2.35% Bull’s eye score improvement and 19.8% NMRR score improvement for the 10 randomly selected trademarks from the MPEG-7 trademarks database.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Engineering
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Uncontrolled Keywords: Trademarks; Image retrieval; Feature extraction; Feature matching; Intellectual Property Management; Knowledge Management
Publisher: Elsevier
ISSN: 0957-4174
Last Modified: 04 Jun 2017 04:20
URI: http://orca-mwe.cf.ac.uk/id/eprint/37093

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