Minimizing the Search Space for Shape Retrieval Algorithms

  • M. Abdullah-Al-Wadud
  • Oksam Chae
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)


To provide satisfactory accuracy and flexibility, most of the existing shape retrieval methods make use of different alignments and translations of the objects that introduce much computational complexity. The most computationally expensive part of these algorithms is measuring the degree of match (or mismatch) of the query object with the objects stored in database. In this paper, we present an approach to cut down a large portion of this search space (number of objects in database) that retrieval algorithms need to take into account. This method is applicable in clustering based approaches also. Moreover, this minimization is done keeping the accuracy of the retrieval algorithms intact and its efficiency is not severely affected in high dimensionalities.


Search Space Retrieval Algorithm Shape Matcher Dissimilarity Index Retrieval Phase 
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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  1. 1.
    Greenspan, M., Godin, G., Talbot, J.: Acceleration of binning nearest neighbor methods. In: Vision Interface, Montreal, Canada, pp. 337–344 (2000)Google Scholar
  2. 2.
    Greenspan, M., Godin, G.: A Nearest Neighbor Method for Efficient ICP. In: 3DIM01: Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Quebec, Canada (2001)Google Scholar
  3. 3.
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The QBIC system. IEEE Computer 28, 23–32 (1995)Google Scholar
  4. 4.
    Zhu, S.C., Yuille, A.L.: FORMS: A flexible object recognition and modelling system. Internat. J. Computer Vision 20(3), 187–212 (1996)Google Scholar
  5. 5.
    Chen, S.W., Tung, S.T., Fang, C.Y., Cherng, S., Jain, A.K.: Extended attributed string matching for shape recognition. Computer Vision and Image Understanding 70(1), 36–50 (1998)CrossRefGoogle Scholar
  6. 6.
    Gdalyahu, Y., Weinshall, D.: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Trans. Pattern Analysis and Machine Intell. 21(12), 1312–1328 (1999)CrossRefGoogle Scholar
  7. 7.
    Latecki, L., Lak€amper, R.: Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1185–1190 (2000)Google Scholar
  8. 8.
    Stein, F., Medioni, G.: Structural indexing: efficient 2-D object recognition. IEEE Trans. Pattern Anal. Machine Intell. 14(12), 1198–1204 (1992)CrossRefGoogle Scholar
  9. 9.
    Super, B.J.: Fast retrieval of isolated visual shapes. Computer Vision and Image Understanding 85(1), 1–21 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Chuang, G.C.-H., Kuo, C.-C.J.: Wavelet descriptor of planar curves: theory and applications. IEEE Transactions Image Process 5(1), 56–70 (1996)CrossRefGoogle Scholar
  11. 11.
    Mokhtarian, F., Abbasi, S., Kittler, J.: Efficient and robust retrieval by shape content through curvature scale space. In: Smeulders, A., Jain, R. (eds.) Image Databases and Multi-Media Search, pp. 51–58. World Scientific, New Jersey (1997)Google Scholar
  12. 12.
    Weber, R., Schek, H.-J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Widom, J., Gupta, A., Shmueli, O. (eds.) VLDB 1998, Proceedings of 24th International Conference on Very Large Data Bases, pp. 24–27 (1998)Google Scholar
  13. 13.
    Li, W., Salari, E.: Successive Elimination Algorithm for Motion Estimation. IEEE transactions on image processing 4(1), 105–107 (1995)CrossRefGoogle Scholar
  14. 14.
    Salari, E., Li, W.: A Fast Quadtree Motion Segmentation for Image Sequence Coding. J. Signal Processing: Image Communication 14, 811–816 (1999)CrossRefGoogle Scholar
  15. 15.
    Santini, S., Jain, R.: Similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 871–883 (1999)CrossRefGoogle Scholar
  16. 16.
    Super, B.J.: Fast Correspondence-based System for Shape Retrieval. Pattern Recognition Letters 25(2), 217–225 (2004)CrossRefGoogle Scholar
  17. 17.
    Beyer, K.S., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is ‘Nearest Neighbor’ Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  18. 18.
    Berchtold, S., Böhm, C., Braunmüller, B., Keim, D.A., Kriegel, H.P.: Fast parallel similarity search in multimedia databases. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Tucson, USA, pp. 1–12 (1997)Google Scholar
  19. 19.
    Berchtold, S., Keim, D., Kriegel, H.P.: The X-tree: An index structure for high_dimensional data. In: Proc. of the Int. Conference on Very Large Databases, pp. 28–39 (1996)Google Scholar
  20. 20.
    Katayama, N., Satoh, S.: The SR-tree: An index structure for high-dimensional nearest neighbor queries. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Tucson, USA, pp. 369–380 (1997)Google Scholar
  21. 21.
    Chen, J.-Y., Bouman, C.A., Allebach, J.P.: Fast image database search using tree-structured vq. In: Proceedings of the International Conference on Image Processing, Santa Barbara, CA, October 1997, pp. 26–29 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Abdullah-Al-Wadud
    • 1
  • Oksam Chae
    • 1
  1. 1.Department of Computer EngineeringGraduate School of Kyung Hee UniversityKyunggi-doKorea

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