Wuhan University Journal of Natural Sciences

, Volume 12, Issue 5, pp 907–911 | Cite as

Combining block and corner features for content-based trademark retrieval

Article

Abstract

In order to retrieve a similarly look trademark from a large trademark database, an automatic content based trademark retrieval method using block hit statistic and corner Delaunay Triangulation features was proposed. The block features are derived from the hit statistic on a series of concentric ellipse. The corners are detected based on an enhanced SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm and the Delaunay Triangulation of corner points are used as the corner features. Experiments have been conducted on the MPEG-7 Core Experiment CE-Shape-1 database of 1 400 images and a trademark database of 2 000 images. The retrieval results are very encouraging.

Key words

content-based image retrieval trademark concentric ellipse enhanced SUSAN 

CLC number

TP 393 

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References

  1. [1]
    Jain A K, Vailaya A. Shape-Based Retrieval: a Case Study with Trademark Image Databases [J]. Pattern Recognition, 2000, 33(2):350–350.CrossRefGoogle Scholar
  2. [2]
    Kim Y S, Kim W Y. Content-Based Trademark Retrieval System Using Visually Salient Features[C]//IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC. USA: IEEE Computer Society, 1998:931–939.Google Scholar
  3. [3]
    Bober M. MPEG-7 Visual Shape Descriptors [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(6):716–719.CrossRefGoogle Scholar
  4. [4]
    Wang Shaoyu, Qi Feihu, Li Huaqing. Minimum Description Length Shape Model Based on Elliptic Fourier Descriptors [C]//Third International Symposium Neural Networks, Chengdu, China, May28–June 1, 2006:645–651.Google Scholar
  5. [5]
    Shih J L, Chen L H. A New System for Trademark Segmentation and Retrieval [J]. Image and Vision Computing, 2001, 19:1011–1018.CrossRefGoogle Scholar
  6. [6]
    Hu M K. Visual Pattern Recognition by Moment Invariants[J]. IRE Trans Info Theory, 1962, IT-8:179–187.Google Scholar
  7. [7]
    Xia T, Zhu H, Shu H, et al. Image Description with Generalized Pseudo-Zernike moments [J]. The Optical Society of America a-Optics Image Science and Vision, 2007, 24(1): 50–59.CrossRefGoogle Scholar
  8. [8]
    Pang Yinghan, Teoh A, Ngo D. Enhanced Pseudo Zernike Moments in Face Recognition [J]. Ieice Electronics Express, 2005, 2(3):70–75.CrossRefGoogle Scholar
  9. [9]
    Xin Y Q, Pawlak M, Liao S. Accurate Computation of Zernike Moments in Polar Coordinates [J]. IEEE Transactions on Image Processing, 2007, 16(2): 581–587.CrossRefGoogle Scholar
  10. [10]
    Shahabi C, Safar M. An Experimental Study of Alternative Shape-Based Image Retrieval Techniques [J]. Multimedia Tools and Applications, 2007, 32(1):29–48.CrossRefGoogle Scholar
  11. [11]
    Shen D F, Jin L, Hsuan T, et al. Trademark Retrieval Based on Block Feature Index Code[C]//IEEE International Conference on Image Processing. Enova Italy: IEEE Press, 2005, 3:77–80.Google Scholar
  12. [12]
    Otsu N. A Threshold Selection Method from Gray-Level Histogram[J]. IEEE Transactions on System Man Cybemet. 1979: 62–66.Google Scholar
  13. [13]
    Kumar P, Yildirim E A. Minimum-Volume Enclosing Ellipsoids and Core Sets [J]. Journal of Optimization Theory and Application, 2005, 126(1):1–21.MATHCrossRefMathSciNetGoogle Scholar
  14. [14]
    Harris C, Stephens M. A Combined Corner and Edge Detector[ C]//In Proceedings of the Fourth Alvey Virion Conference, Manchester: Univ of Manchester, 1988: 147–151.Google Scholar
  15. [15]
    Smith S M, Brady J M. SUSAN—A New Approach to Low Level Image Processing [J]. International Journal of Computer Vision, 1997, 23(1):45–78.CrossRefGoogle Scholar
  16. [16]
    He Xiaochen, Yung N H C. Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support[C]//Proceedings of 17th International Conference on Pattern Recognition (ICPR’04). Washington, DC. USA: IEEE Computer Society, 2004, 2:791–794.Google Scholar
  17. [17]
    Tao Yi, Grosky W I. Delaunay Triangulation for Image Object Indexing: A Novel Method for Shape Representation[C]//Proceedings of IS&T/SPIE Symposium on Storage and Retrieval for Image and Video Databases VII, California: Univ of Wayne State, 1998:631–642.Google Scholar
  18. [18]
    Dwyer R A. A Faster Divide-and-Conquer Algorithm for Constructing Delaunay Triangulations [J]. Algorithmic, 1987, 2(2): 127–151.MathSciNetGoogle Scholar

Copyright information

© Wuhan University 2007

Authors and Affiliations

  • Hong Zhiling 
    • 1
  • Jiang Qingshan 
    • 2
  • Wu Meihong 
    • 1
  1. 1.School of Information Science and TechnologyXiamen UniversityXiamenChina
  2. 2.School of SoftwareXiamen UniversityXiamenChina

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