Content-based trademark retrieval system using a visually salient feature

Abstract The ever-increasing number of registered trademarks has created greater demand for an automatic trademark retrieval system. In this paper, we present a method for such a system based on the image content, using a shape feature. Zernike moments of an image are used for a feature set. To retrieve similarly shaped trademarks quickly, we introduced the concept of a `visually salient feature' that dominantly affects the global shape of the trademarks. Experiments have been conducted on a database of 3000 trademark images. The retrieval speed was very fast and similar-shaped trademark retrieval results were very promising.

[1]  H. V. Jagadish,et al.  A retrieval technique for similar shapes , 1991, SIGMOD '91.

[2]  Toshikazu Kato,et al.  Database architecture for content-based image retrieval , 1992, Electronic Imaging.

[3]  Alberto Del Bimbo,et al.  Image indexing using shape-based visual features , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[4]  Anil K. Jain,et al.  Image databases: a case study in Norwegian silver authentication , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[5]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[7]  Gian Antonio Mian,et al.  Trademark shapes description by string-matching techniques , 1994, Pattern Recognit..

[8]  Andrea Clematis,et al.  A system architecture for fault tolerance in concurrent software , 1990, Computer.

[9]  Alireza Khotanzad,et al.  Rotation invariant image recognition using features selected via a systematic method , 1990, Pattern Recognition.

[10]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  John P. Eakins Retrieval of trade mark images by shape feature , 1994 .

[12]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Josef Kittler,et al.  Efficient and Robust Retrieval by Shape Content through Curvature Scale Space , 1998, Image Databases and Multi-Media Search.

[14]  John P. Eakins,et al.  ARTISAN: a shape retrieval system based on boundary family indexing , 1996, Electronic Imaging.

[15]  M. Teague Image analysis via the general theory of moments , 1980 .

[16]  Josef Bigün,et al.  Orientation radiograms for image retrieval: an alternative to segmentation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[17]  Richard J. Prokop,et al.  A survey of moment-based techniques for unoccluded object representation and recognition , 1992, CVGIP Graph. Model. Image Process..

[18]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[19]  Whoi-Yul Kim,et al.  A practical pattern recognition system for translation, scale and rotation invariance , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  S. Sitharama Iyengar,et al.  A New Method of Image Compression using Irreducible Covers of Maximum Rectangles , 1988, IEEE Trans. Software Eng..

[21]  Babu M. Mehtre,et al.  STAR-A System for Trademark Archival and Retrieval , 1995 .

[22]  Whoi-Yul Kim An Analytical and Experimental Study of Binary Image Normalization for Scale Invariance with Zernike Moments , 1997 .

[23]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Anil K. Jain,et al.  A hierarchical system for efficient image retrieval , 1996, Proceedings of 13th International Conference on Pattern Recognition.