Indexing and Retrieving High Dimensional Visual Features

  • Jesse S. Jin
Part of the Signals and Communication Technology book series (SCT)


In this chapter, we review high-dimensional retrieval by examining the chronological evolution of various indexing techniques. We then discuss the R-tree which leads to the development of CSS+-tree. Two important aspects of high dimensional index and retrieval, namely varying distance metrics and dimension reduction are, also discussed and some creative solutions proposed.


Hide Layer Range Query Hybrid Network Index Tree Hilbert Curve 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Arya, S. (1995). Nearest Neighbor Searching and Applications. PhD thesis, Computer Vision Laboratory, University of Maryland, 20742 - 3275.Google Scholar
  2. [2]
    Bai, X., Xu, G., Jin, J. S., & Kurniawati, R. (1997). Content based retrieval and related techniques. Robot 19: 231 - 240.Google Scholar
  3. [3]
    Baldi P & Hornik K (1989). Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks; 2: 53 - 58.CrossRefGoogle Scholar
  4. [4]
    Bayer, R. & McCreight, E. (1972). Organization and maintenance of large ordered indexes. Acta Informatica, 1 (3): 173 - 189.CrossRefGoogle Scholar
  5. [5]
    Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990). The R-tree: an efficient and robust access method for points and rectangles. Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pp. 322 - 331.Google Scholar
  6. [6]
    Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18 (9): 509 - 517.MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    Bentley, J. L. & Stanat, D. F. (1975). Analysis of range searches in quad trees. Information Processing Letters, 3 (6): 170 - 173.MathSciNetMATHCrossRefGoogle Scholar
  8. [8]
    Berchtold, S. & Keim, D. A. (1998). Indexing high-dimensional space: database support for next decade’s applications. In ACM-SIGMOD’98 Tutorial,Seattle, Washington. ACM.Google Scholar
  9. [9]
    Berchtold, S., Böhm, C., & Kriegel, H.-P. (1998). The pyramid-tree: Breaking the curse of dimensionality. Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 78 - 86, Seattle, Washington.Google Scholar
  10. [10]
    Berchtold, S., Keim, D. A., & Kriegel, H.-P. (1996). The X-tree: An index structure for high-dimensional data. Proc. 22th Int. Conf. on Very Large Data Bases, pages 28 - 39, Bombay, India.Google Scholar
  11. [11]
    Brin, S. (1995). Near neighbor search in large metric spaces. In VLDB 1995.Google Scholar
  12. [12]
    Cormen, T., Leiserson, C., & Rivest, R. (1990). Introduction to Algorithms. The MIT Press, MIT, Cambridge.Google Scholar
  13. [13]
    Duda, R. O. & Hart, P. E. (1973). Pattern Classification and Scene Analysis. John Wiley & Sons, New York.MATHGoogle Scholar
  14. [14]
    Ellis, T., Roussopoulos, N., & Faloutsos, C. (1987). The R+tree: A dynamic index for multi-dimensional objects. Proc. the 13th Conf. on VLDB, pp.507518.Google Scholar
  15. [15]
    Faloutsos, C. (1996). Searching Multimedia Databases by Content. Advances in Database Systems. Kluwer Academic Publishers, Boston.Google Scholar
  16. [16]
    Faloutsos, C., Equitz, W., Flickner, M., Niblack, W., Petkovic, D., & Barber, R. (1994). Efficient and effective querying by image content. J. of Intelligent Information Systems, 3: 231 - 262.CrossRefGoogle Scholar
  17. [17]
    Finkel, R. A. & Bentley, J. L. (1974). Quad trees: A data structure for retrieval on composite keys. Acta Informatica, Springer Verlag (Heidelberg, FRG and NewYork NY, USA) Verlag, 4.Google Scholar
  18. [18]
    Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., & Yanker, P. (1995). Query by image and video content: The QBIC system. IEEE Computer, pp. 23 - 32.Google Scholar
  19. [19]
    Freeston, M. (1987). The bang file: a new kind of grid file. Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pages 260 - 269.Google Scholar
  20. [20]
    Friedman, J. H., Baskett, F., & Shustek, L. H. (1975). An algorithm for finding nearest neighbors. IEEE Trans. on Computers, C-24: 1000 - 1006.Google Scholar
  21. [21]
    Friedman, J. H., Bentley, J. L., & Finkel, R. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Trans. on Math. Software, 3 (3): 209 - 226.MATHCrossRefGoogle Scholar
  22. [22]
    Fukunaga, K. & Narendra, P. M. (1975). A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. on Computers, C-24(7): 750 - 753.Google Scholar
  23. [23]
    Gaede, V. & Günther, O. (1995). Multidimensional access methods. Technical Report ISS-16,Institute of Information Systems, Humboldt-Universitat zu Berlin, Spandauer Str. 1 D-10178 Berlin. Google Scholar
  24. [24]
    Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. Proc. the ACM SIGMOD Int. Conf on Management of Data, pp.47-57, Boston.Google Scholar
  25. [25]
    Hertz J, Krogh A & Palmer R. Introduction to the theory of Neural Computation. Addison-Wesley, Redwood City 1991.Google Scholar
  26. [26]
    Hjaltason, G. R. & Samet, H. (1995). Ranking in spatial databases. In Egen hofer, M. J. & Herring, J. R., editors, Advances in Spatial Databases, 4th International Symposium, SSD’95, volume 951 of Lecture Notes in Computer Science, pp.83-95, Berlin. Springer-Verlag.Google Scholar
  27. [27]
    Ievergelt, J., Hinterberger, H., and Sevcik, K. C. (1984). The grid file: an adaptable symmetric multikey file structure. ACM Transactions on Database Systems,9(1). Google Scholar
  28. [28]
    Jagadish, H. (1990). Linear clustering of objects with multiple attributes. Proc. the ACM SIGMOD Int. Conf. on Management of Data,pp.332-342, Atlantic City. Google Scholar
  29. [29]
    Jin, JS; Greenfield, H & Kurniawati, R (1997). CBIR-VU: a new scheme for processing visual data in multimedia systems. Lecture Notes in Computer Science: Visual Information Systems, pp. 40 - 65.Google Scholar
  30. [30]
    Jin, J S & Kurniawati, R (2001). Varying similarity metrics in visual information retrieval. Pattern Recognition Letters,22(5):583-592.Google Scholar
  31. [31]
    Jin, J. S., Kurniawati, R., and Xu, G. (1996). A scheme for intelligent image retrieval in multimedia databases. Journal of Visual Communication and Image Representation, 7 (4): 369 - 377.CrossRefGoogle Scholar
  32. [32]
    Jin, J., Tiu, L. S., and Tam, S. W. S. (1995). Partial image retrieval in multimedia databases. Proceedings of Image and Vision Computing New Zealand, pages 179 - 184, Christchurch. Industrial Research Ltd.Google Scholar
  33. [33]
    Joliffe I.Principal Component Analysis. Springer-Verlag, New York 1986. Google Scholar
  34. [34]
    Kambhatla N & Leen T. Fast Non-linear dimension reduction. In: Cowan J, Tesauro G, Alspector J (eds). Advances In Neural Information Processing Systems 6. Morgan Kaufman, San Mateo, CA 1994. Google Scholar
  35. [35]
    Knuth, D. (1998). The Art of Computer Programming, Vol. 3: Sorting and Searching. Addison-Wesley, Reading, Mass, 2nd edition.Google Scholar
  36. [36]
    Korn, F., Sidiropoulos, N., Faloutsos, C., and Siegel, E. (1996). Fast nearest neighbor search in medical image databases. International Conference on Very Large Data Bases,Bombay, India. Google Scholar
  37. [37]
    Kramer M. Nonlinear principal component analysis using autoassociative neural networks. AIChe Journal 1991; 37: 233 - 243.CrossRefGoogle Scholar
  38. [38]
    Kurniawati, R., Jin, J. S., and Shepherd, J. A. (1997). The SS+-tree: An improved index structure for similarity searches in a high dimensional feature space. Proceedings of the SPIE: Storage and Retrieval for Image and Video Databases V, volume 3022, pages 110 - 120, San Jose, CA.Google Scholar
  39. [39]
    Kurniawati, R., Jin, J., and Shepherd, J. A. (1997). Techniques for supporting efficient content-based retrieval in multimedia databases. The Australian Computer Journal, 29 (4): 122 - 130.Google Scholar
  40. [40]
    Lin, K.-I., Jagadish, H. V., and Faloutsos, C. (1994). The TV tree: An index structure for high-dimensional data. VLDB Journal, 3(4): 517 549.Google Scholar
  41. [41]
    Ma, N. L. & Jin, J. S. (1998). A generalized content-based image retrieval system. Proc. of ACM Symposium on Applied Computing, pp.460-461, Atlanta.Google Scholar
  42. [42]
    Nene, S. A. & Nayar, S. K. (1997). A simple algorithm for nearest neighbor search in high dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 19. Google Scholar
  43. [43]
    Nievergelt, J; Hinterberger, H; & Sevcik, K C (1984). The grid file: an adaptable symmetric multikey file structure. ACM Transactions on Database Systems,9(1). Google Scholar
  44. [44]
    Orenstein, J. & Merrett, T. (1984). A class of data structures for associative searching. Proc. of 3rd SIGACT-SIGMOD symposium on principles of database systems,pages 181-190, Waterloo, Ontario, Canada. Google Scholar
  45. [45]
    Pandya A & Macy R. Pattern Recognition With Neural Network In C++. CRC Press 1996.Google Scholar
  46. [46]
    Robinson, J. T. (1981). The K-D-B-tree: A search structure for large multidimensional dynamic indices. Proceedings of the ACM SIG MOD International Conference on Management of Data, pp.10-18, Ann Arbor, MI. Google Scholar
  47. [47]
    Roussopoulos, N., Kelley, S., and Vincent, F. (1995). Nearest neighbor queries. pages 71 - 79, San Jose, California.Google Scholar
  48. [48]
    Samet, H. (1990). The Design and Analysis of Spatial Data Structures. Addison Wesley.Google Scholar
  49. [49]
    Sellis, T; Roussopoulos, N & Faloutsos, C (1987). The R+-tree: a dynamic index for multi-dimensional objects. Proceedings of the Thirteenth Conference on Very Large Databases,Los Altos, CA, pp.507-518. Google Scholar
  50. [50]
    Shepherd, J. A., Megiddo, N., and Zhu, X. (1998). Making QBIC faster. Technical Report, IBM Almaden Research Center.Google Scholar
  51. [51]
    Smoliar, S. W. & Zhang, H. J. (1994). Content-based video indexing and retrieval. IEEE Multimedia, 1 (2): 62 - 72.CrossRefGoogle Scholar
  52. [52]
    Sproull, R. F. (1991). Refinements to nearest-neighbor searching in k dimensional trees. Algorithmica, 6: 579 - 589.MathSciNetMATHCrossRefGoogle Scholar
  53. [53]
    Watson, A. B. (1983). Detection and recognition of simple spatial forms. In Braddick, O. J. and Sleigh, A. A., editors, Physical and biological processing of images,pages 101-114. Springer-Verlag, New York. Google Scholar
  54. [54]
    Weber, R; Schek, H J & Blott, S (1998). A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. The Proceedings of the 24th Int. Conf on VLDB, pp. 194 - 205.Google Scholar
  55. [55]
    White, D. A. & Jain, R. (1996). Algorithms and strategies for similarity retrieval. Technical Report VCL-96-01, Visual Computing Laboratory, University of California, San Diego.Google Scholar
  56. [56]
    White, D. A. & Jain, R. (1996). Similarity indexing with the SS-tree. Proc. 12th IEEE International Conference on Data Engineering,New Orleans, Louisiana. Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jesse S. Jin

There are no affiliations available

Personalised recommendations