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A Complete Keypics Experiment with Size Functions

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Image and Video Retrieval (CIVR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3568))

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Abstract

Keypics are graphical metadata intended for indexing of images on the Internet. They are conceived as hand-drawn sketches, not restricted to a definite set. An obvious difficulty when dealing with keypics is that they elude rigid geometric treatment.

A proposal of solution comes from Size Functions. This paper is the report of a complete experiment on 494 keypics with Size Functions based on three measuring functions (distances, projections and jumps) and their combination.

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References

  1. http://w3.org/Graphics/SVG/About.html

  2. Carlsson, S.: Order structure, correspondence, and shape based categories. In: Forsyth, D.A., et al. (eds.) Shape, Contour, and Grouping 1999. LNCS, vol. 1681, pp. 58–71. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Cerri, A., Ferri, M., Giorgi, D.: A New Framework for Trademark Retrieval Based on Size Functions. In: To appear on: Proc. 2nd International Conference on Vision, Video and Graphics, Heriot Watt University, Edinburgh (July 7-8, 2005)

    Google Scholar 

  4. d’Amico, M.: A New Optimal Algorithm for Computing Size Functions of Shapes. In: CVPRIP Algorithms III, Proc. Intl. Conf. on Computer Vision, Pattern recognition and Image Processing, Atlantic City, pp. 107–110 (2000)

    Google Scholar 

  5. Donatini, P., Frosini, P., Landi, C.: Deformation energy for size functions. In: Hancock, E.R., Pelillo, M. (eds.) EMMCVPR 1999. LNCS, vol. 1654, pp. 44–53. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  6. Ferri, M., Frosini, P.: Range size functions. In: Proc. SPIE Conf. on Vision Geometry III, Boston, November 2–3, pp. 243–251 (1994)

    Google Scholar 

  7. Ferri, M., Frosini, P.: A proposal for image indexing: “keypics”, plastic graphical metadata. In: Proc. IS&T/SPIE Symp. on Electronic Imaging, Internet Imaging VI, San Jose (January 16–20, 2005)

    Google Scholar 

  8. Frosini, P., Landi, C.: Size functions and formal series. Applicable Algebra in Engineering Communication and Computing 12, 327–349 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  9. Granlund, G.H.: Fourier preprocessing for hand print character recognition. IEEE Trans. Computers C-21, 195–201 (1972)

    Article  MathSciNet  Google Scholar 

  10. Hilaga, M., Shinagawa, Y., Kohmura, T., Kunii, T.L.: Topology matching for fully automatic similarity estimation of 3D shapes. In: SIGGRAPH 2001, Computer Graphics Proc., Annual Conference Series, pp. 203–212 (2001)

    Google Scholar 

  11. Huijsmans, D.P., Sebe, N.: How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope. IEEE Trans. on PAMI 27, 245–251 (2005)

    Google Scholar 

  12. Iivarinen, J., Visa, A.: Shape recognition of irregular objects. In: Casasent, D.P. (ed.) Intelligent Robots and Computer Vision XV: Algorithms, Techniques, Active Vision, and Materials Handling, Proc. SPIE, vol. 2904, pp. 25–32 (1996)

    Google Scholar 

  13. Leung, M.-W., Chan, K.-L.: Object–based image retrieval using hierarchical shape descriptor. In: Lew, M.S., Sebe, N., Eakins, J.P. (eds.) CIVR 2002. LNCS, vol. 2383, pp. 165–174. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  14. Liu, W., Su, Z., Li, S., Zhang, H.J.: A Performance Evaluation Protocol for Content-Based Image Retrieval Algorithms/Systems. In: Proc. IEEE CVPR Workshop on Empirical Evaluation in Computer Vision, Kauai, USA (December 2001)

    Google Scholar 

  15. Müller, H., Müller, W., Squire, D.M., Marchand–Maillet, S., Pun, T.: Performance evaluation in content–based image retrieval: Overview and proposals. Pattern Rec. Letters 22, 593–601 (2001)

    Article  MATH  Google Scholar 

  16. Petkovic, D., Jain, R.C.: Visual Information systems: lessons for its future. In: Proc. IS&T/SPIE Symp. on Electronic Imaging, Internet Imaging VI, San Jose (January 16–20, 2005)

    Google Scholar 

  17. Veltcamp, R.C., Hagedoorn, M.: State–of–the–art in shape matching. In: Lew, M. (ed.) Principles of Visual Information Retrieval, pp. 87–119. Springer, Heidelberg (2001)

    Google Scholar 

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Cerri, A., Ferri, M., Giorgi, D. (2005). A Complete Keypics Experiment with Size Functions. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_39

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  • DOI: https://doi.org/10.1007/11526346_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27858-0

  • Online ISBN: 978-3-540-31678-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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