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VIRMA: Visual Image Retrieval by Shape MAtching

  • G. Castellano
  • C. Castiello
  • A. M. Fanelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

The huge amount of image collections connected to multimedia applications has brought forth several approaches to content-based image retrieval, that means retrieving images based on their visual content instead of textual descriptions. In this paper, we present a system, called VIRMA (Visual Image Retrieval by Shape MAtching), which combines different techniques from Computer Vision to perform content-based image retrieval based on shape matching. The architecture of the VIRMA system is portrayed and algorithms underpinning the developed prototype are briefly described. Application of VIRMA to a database of real-world pictorial images shows its effectiveness in visual image retrieval.

Keywords

Fuzzy Rule Image Retrieval Shape Match Retrieval Accuracy Image Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arkin, E., Chew, P., Huttenlocher, D., Kedem, K., Mitchel, J.: An efficiently computable metric for comparing polygonal shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(3), 209–215 (1991)CrossRefGoogle Scholar
  2. 2.
    Berretti, S., D’Amico, G., Del Bimbo, A.: Shape representation by spatial partitioning for content based retrieval applications. In: Proc. of IEEE International Conference on Multimedia and Expo (ICME 2004), pp. 791–794 (2004)Google Scholar
  3. 3.
    Berretti, S., Del Bimbo, A., Pala, P.: Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. on Multimedia 2(4), 225–239 (2000)CrossRefGoogle Scholar
  4. 4.
    Binaghi, E., Gagliardi, I., Schettini, R.: Image retrieval using fuzzy evaluation of color similarity. Int. J. Pattern Recognition and Art. Int. 8(4), 945–968 (1994)CrossRefGoogle Scholar
  5. 5.
    Castellano, G., Castiello, C., Fanelli, A.M.: Content-based image retrieval by shape matching. In: Proc. of North American Fuzzy Information Processing Society (NAFIPS 2006) (2006)Google Scholar
  6. 6.
    Castiello, C., Castellano, G., Caponetti, L., Fanelli, A.M.: Classifying image pixels by a neuro-fuzzy approach. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 253–256. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Del Bimbo, A., Mugnaini, M., Pala, P., Turco, F.: Visual querying by color perceptive regions. Pattern Recognition 31, 1241–1253 (1998)CrossRefGoogle Scholar
  8. 8.
    Del Bimbo, A., Pala, P.: Visual image retrieval by elastic matching of user sketches. IEEE Trans. Pattern Anal. Machine Intell. 19, 121–132 (1997)CrossRefGoogle Scholar
  9. 9.
    Eidenberger, H.: A new perspective on Visual Information Retrieval. In: SPIE IS&T Electronic Imaging Conference, San Jose, USA (2004)Google Scholar
  10. 10.
    Huang, T.S., Rui, Y.: Image Retrieval: Current Techniques, Promising Directions And Open Issues. Journal of Visual Comm. and Image Repr. 10(4), 39–62 (1999)Google Scholar
  11. 11.
    Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition 29(8), 1233–1244 (1996)CrossRefGoogle Scholar
  12. 12.
    Kovesi, P.D.: MATLAB and Octave Functions for Computer Vision and Image Processing. School of Computer Science and Software Engineering, The University of Western AustraliaGoogle Scholar
  13. 13.
    Latecki, L.J., Lakamper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. on Pattern Analysis and Mach. Intell. 22, 1–6 (2000)CrossRefGoogle Scholar
  14. 14.
    Long, F., Zhang, H.J., Feng, D.D.: Fundamentals of Content-Based Image Retrieval. In: Feng, D., Siu, W.C., Zhang, H.J. (eds.) Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, Springer, Heidelberg (2003)Google Scholar
  15. 15.
    Liu, F., Picard, R.W.: Periodicity, directionality, and randomness-Wold features for image modeling and retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 18, 722–733 (1996)CrossRefGoogle Scholar
  16. 16.
    Morandi, M.: web page: http://www.museomorandi.it
  17. 17.
    Pala, P., Santini, S.: Image retrieval by shape and texture. Pattern Recognition 32, 517–527 (1999)CrossRefGoogle Scholar
  18. 18.
    Santini, S., Jain, R.: Beyond Query by Example. In: ACM Multimedia 1998, Bristol, UK, pp. 345–350. ACM, New York (1998)CrossRefGoogle Scholar
  19. 19.
    Zhou, S., Venkatesh, Y.V., Ko, C.C.: Texture retrieval using tree-structured wavelet transform. In: Proc. of Int. Conference on Computer Vision, Pattern Recognition, and Image Processing (CVPRIP 2000) (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • G. Castellano
    • 1
  • C. Castiello
    • 1
  • A. M. Fanelli
    • 1
  1. 1.Computer Science DepartmentUniversity of BariBariItaly

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