Generic Shape Classification for Retrieval

  • Manuel J. Fonseca
  • Alfredo Ferreira
  • Joaquim A. Jorge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3926)

Abstract

We present a shape classification technique for structural content–based retrieval of two-dimensional vector drawings. Our method has two distinguishing features. For one, it relies on explicit hierarchical descriptions of drawing structure by means of spatial relationships and shape characterization. However, unlike other approaches which attempt rigid shape classification, our method relies on estimating the likeness of a given shape to a restricted set of simple forms. It yields for a given shape, a feature vector describing its geometric properties, which is invariant to scale, rotation and translation. This provides the advantage of being able to characterize arbitrary two–dimensional shapes with few restrictions. Moreover, our technique seemingly works well when compared to established methods for two dimensional shapes.

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References

  1. 1.
    Boyce, J.E., Dobkin, D.P.: Finding Extremal Polygons. SIAM Journal on Computing 14(1), 134–147 (1985)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Cvetkovic, D., Rowlinson, P., Simic, S.: Eigenspaces of Graphs. Cambridge University Press, United Kingdom (1997)CrossRefMATHGoogle Scholar
  3. 3.
    Davies, E.R.: Machine Vision: Theory, Algorithms, Practicalities. Academic Press, London (1997)Google Scholar
  4. 4.
    Fonseca, M.J.: Sketch-Based Retrieval in Large Sets of Drawings. PhD thesis, Instituto Superior Técnico / Universidade Técnica de Lisboa (July 2004)Google Scholar
  5. 5.
    Fonseca, M.J., Barroso, B., Ribeiro, P., Jorge, J.A.: Retrieving ClipArt Images by Content. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 500–507. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Fonseca, M.J., Ferreira, A., Jorge, J.A.: Towards 3D Modeling using Sketches and Retrieval. In: Proceedings of the first Eurographics Workshop on Sketch–Based Interfaces and Modeling, Grenoble, France. EG Publishing (2004)Google Scholar
  7. 7.
    Fonseca, M.J., Jorge, J.A.: CALI: A Software Library for Calligraphic Interfaces. INESC-ID (2000), Available at: http://immi.inesc-id.pt/cali/
  8. 8.
    Fonseca, M.J., Jorge, J.A.: Experimental Evaluation of an on-line Scribble Recognizer. Pattern Recognition Letters 22(12), 1311–1319 (2001)CrossRefMATHGoogle Scholar
  9. 9.
    Fonseca, M.J., Jorge, J.A.: Experimental evaluation of an on-line scribble recognizer. Pattern Recognition Letters 22(12), 1311–1319 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Fonseca, M.J., Ferreira Jr., A., Jorge, J.A.: Content-Based Retrieval of Technical Drawings. International Journal of Computer Applications in Technology (IJCAT) 23(2/3/4), 86–100 (2005)CrossRefGoogle Scholar
  11. 11.
    Freeman, H., Saghri, A.: Generalized Chain Codes for Planar Curves. In: Proceedings of the International Joint Conference on Pattern Recognition, Kyoto, Japan, November 1978, pp. 701–703 (1978)Google Scholar
  12. 12.
    Freeman, H., Shapira, R.: Determining the Minimum-area Encasing Rectangle for an Arbitrary Closed Curve. Communications of the ACM 18(7), 409–413 (1975)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Gu, C.: Multivalued Morphology and Segmentation-based Coding. Phd thesis, Signal Processing Lab. of Swiss Federal Institute of Technology at Lausanne (1995)Google Scholar
  14. 14.
    Hu, M.-K.: Visual Pattern Recognition by Moment Invariants. IRE Transactions on Information Theory 8, 179–187 (1962)MATHGoogle Scholar
  15. 15.
    Kauppinen, H., Seppanen, T., Pietikainen, M.: An Experimental Comparison of Autoregressive and Fourier-Based Descriptors in 2D Shape Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 17(2), 201–207 (1995)CrossRefGoogle Scholar
  16. 16.
    Lu, G.J., Sajjanhar, A.: Region-Based Shape Representation and Similarity Measure Suitable for Content-Based Image Retrieval. Multimedia Systems 7, 165–174 (1999)CrossRefGoogle Scholar
  17. 17.
    Mehtre, B.M., Kankanhali, M.S., Lee, W.F.: Shape Measures for Content Based Image Retrieval: A Comparison. Information Processing and Management 33(3), 319–337 (1997)CrossRefGoogle Scholar
  18. 18.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and Robust Retrieval by Shape Content through Curvature Scale Space. In: International Workshop on Image Databases and Nultimedia Search, Amsterdam, The Netherlands, pp. 35–42 (1996)Google Scholar
  19. 19.
    O’Rourke, J.: Computational Geometry in C, 2nd edn. Cambridge University Press, Cambridge (1998)CrossRefMATHGoogle Scholar
  20. 20.
    Persoon, E., Fu, K.-S.: Shape Discrimination Using Fourier Descriptors. IEEE Trans. on Systems, Man and Cybernetics 7(3), 170–179 (1977)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Safar, M., Shahabi, C., Tan, C.h.: Resiliency and Robustness of Alternative Shape-Based Image Retrieval Techniques. In: Proceedings of IEEE International Database Engineering and Applications Symposium (IDEAS 2000), pp. 337–348 (2000)Google Scholar
  22. 22.
    Safar, M., Shahabi, C., Sun, X.: Image Retrieval By Shape: A Comparative Study. In: Proceedings of the IEEE International Conference on Multimedia and Exposition (ICME 2000), pp. 141–144 (2000)Google Scholar
  23. 23.
    Zhang, D.S., Lu, G.: A comparative study of curvature scale space and fourier descriptors. Journal of Visual Communication and Image Representation 14(1), 41–60 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Manuel J. Fonseca
    • 1
  • Alfredo Ferreira
    • 1
  • Joaquim A. Jorge
    • 1
  1. 1.Department of Information Systems and Computer ScienceINESC-ID/IST/Technical University of LisbonPortugal

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