A System Supporting Semantics Retrieval

  • Yuqing Song
  • Aidong Zhang
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 22)


With the advance of multimedia technology, image data in various formats are becoming available at an explosive rate. With such enormous data resources, the search and retrieval of image databases are demanded to provide open access to relevant information and products. Thus, content-based image retrieval (CBIR) has become an active research area. A variety of techniques have been developed. In particular, content-based image retrieval using low-level features such as colour [36, 34, 26], texture [21, 33, 32, 20], shape [22, 14, 23, 24, 15, 10, 17] and others [30, 2, 16, 7] extracted from the images has been well studied. Various image querying systems including QBIC [11], VisualSeek [34], PhotoBook [27] and Virage [5] have been built based on the low-level features for general or specific image retrieval tasks.


Geographic Information System Image Retrieval Minimal Span Tree Image Database Semantic Feature 
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]
    N. Ahuja. Dot pattern processing using voronoi neighborhoods. IEEE Trans, on Pattern Analysis and Machine Intelligence, 4(3):336–343, 1982.MathSciNetCrossRefGoogle Scholar
  2. [2]
    N. Ahuja and A. Rosenfeld. Mosaic models for texture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(1): 1–11, 1981.CrossRefGoogle Scholar
  3. [3]
    N. Ahuja and M. Tuceryan. Extraction of early perceptual structure in dot patterns: integrating region, boundary, and component gestalt. Computer Vision, Graphics, and Image Processing, 48(3):304–356, 1989.CrossRefGoogle Scholar
  4. [4]
    P. Alshuth, T. Hermes, C. Klauck, J. Krey, and M. Roper. Iris — image retrieval for images and videos. In Proc. of First Int. Workshop of Image Databases and MultiMedia Search, Amsterdam, pages 170–178, 1996.Google Scholar
  5. [5]
    J.R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Jain, and C.F. Shu. The virage image search engine: An open framework for image management. In Proceedings of SPIE, Storage and Retrieval for Still Image and Video Databases IV, pages 76–87, San Jose, CA, USA, February 1996.Google Scholar
  6. [6]
    J. Crowley and A. Parker. A representation of shape based on peaks and ridges in the difference of low-pass transform. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 6(2): 156–169, March 1984.CrossRefGoogle Scholar
  7. [7]
    E.R. Dougherty and J.B. Pelz. Texture-based segmentation by morphological granulometrics. In Advanced Printing of Paper Summaries, Electronic Imaging #2032#89, volume 1, pages 408–414, Boston, Massachusetts, October 1989.Google Scholar
  8. [8]
    Rakesh Dugad and Narendra Ahuja. Unsupervised multidimensional hierarchical clustering. In IEEE International Conference on Acoustics Speech and Signal Processing, Seattle, May 1998.Google Scholar
  9. [9]
    J.P. Eakins. Automatic image content retrieval — are we getting anywhere. In Proc. of 3rd International Conference on Electronic Library and Visual Information Research, pages 123–135, May 1996.Google Scholar
  10. [10]
    C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic, and W. Equitz. Efficient and effective querying by image content. Journal of Intelligent Information Systems, 3(3/4):231–262, July 1994.CrossRefGoogle Scholar
  11. [11]
    M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, and B. Dom et al. Query by Image and Video Content: The QBIC System. IEEE Computer, 28(9):23–32, September 1995.CrossRefGoogle Scholar
  12. [12]
    M.D. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: The qbic system. Computer, 28(9):23–32, September 1995.CrossRefGoogle Scholar
  13. [13]
    D.A. Forsyth, J. Malik, M.M. Fleck, H. Greenspan, T. Leung, S. Belongie, C. Carson, and C. Bregler. Finding pictures of objects in large collections of images. In Report of the NSF/ARPA Workshop on 3D Object Representation for Computer Vision, page 335, 1996.CrossRefGoogle Scholar
  14. [14]
    K. Hirata and T. Kato. Rough sketch-based image information retrieval. NEC Research & Development, 34(2):263–273, 1993.Google Scholar
  15. [15]
    B.K.P. Horn. Robot Vision. The MIT Press, forth edition, 1988.Google Scholar
  16. [16]
    R.W. Fries, J.W. Modestino and A.L. Vickers. Texture discrimination based upon an assumed stochastic texture model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(5):557–580, 1981.CrossRefGoogle Scholar
  17. [17]
    F. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas. Fast nearest-neighbor search in medical image databases. In Conference on Very Large Data Bases (VLDB96), September 1996.Google Scholar
  18. [18]
    Tony Lindeberg. Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, 1994.Google Scholar
  19. [19]
    W.Y. Ma and B.S. Manjunath Netra: A toolbox for navigating large image databases. In International Conference on Image Processing, pages 1:568–571, 1997.Google Scholar
  20. [20]
    B.B. Mandelbrot. Fractals-Form, Chance, Dimension. W.H. Freeman, San Francisco, California, 1977.Google Scholar
  21. [21]
    B.S. Manjunath and W.Y. Ma. Texture Features for Browsing and Retrieval of Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):837–842, August 1996.CrossRefGoogle Scholar
  22. [22]
    Rajiv Mehrotra and James E. Gary. Similar-shape retrieval in shape data management. IEEE Computer, 28(9):57–62, September 1995.CrossRefGoogle Scholar
  23. [23]
    F. Mokhtarian, S. Abbasi, and J. Kittler. Efficient and Robust Retrieval by Shape Content through Curvature Scale Space. In Proc. International Workshop on Image Databases and MultiMedia Search, pages 35–42, Amsterdam, The Netherlands, 1996.Google Scholar
  24. [24]
    F. Mokhtarian, S. Abbasi, and J. Kittler. Robust and efficient shape indexing through curvature scale space. In Proceedings of British Machine Vision Conference, pages 53–62, Edinburgh, UK, 1996.Google Scholar
  25. [25]
    S. Morse. Concepts of use in computer map processing. Communications of the ACM, 12(3): 147–152, March 1969.zbMATHCrossRefGoogle Scholar
  26. [26]
    G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65–73, Boston MA USA, 1996.Google Scholar
  27. [27]
    Pentland, R. Picard, and S. Sclaroff. Photobook: Tools for Contentbased Manipulation of Image Databases. In Proceedings of the SPIE Conference on Storage and Retrieval of Image and Video Databases II, pages 34–47, 1994.Google Scholar
  28. [28]
    A.P. Pentland, R.W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. IJCV, 18(3):233–254, June 1996.CrossRefGoogle Scholar
  29. [29]
    K. Perez-Lopez, A. Sood, and M. Manohar. Selecting Image Subbands for Browsing Scientific Image Databases. In Proceedings SPIE Conference on Wavelet Applications, Orlando, April 1994.Google Scholar
  30. [30]
    R. Picard. A society of models for video and image libraries. Technical Report 360, MIT Media Laboratory Perceptual Computing, 1996.Google Scholar
  31. [31]
    J. Roubal and T.K. Peucker. Automated contour labelling and the contour tree. In Proc. AUTO-CARTO 7, pages 472–481, 1985.Google Scholar
  32. [32]
    G. Sheikholeslami and A. Zhang. An Approach to Clustering Large Visual Databases Using Wavelet Transform. In Proceedings of the SPIE Conference on Visual Data Exploration and Analysis IV, pages 322–333, San Jose, February 1997.Google Scholar
  33. [33]
    J. R. Smith and S. Chang. Transform Features For Texture Classification and Discrimination in Large Image Databases. In Proceedings of the IEEE International Conference on Image Processing, pages 407–411, 1994.Google Scholar
  34. [34]
    John R. Smith and Shih-Fu Chang. VisualSeek: a fully automated content-based image query system. In Proceedings of ACM Multimedia 96, pages 87–98, Boston MA USA, 1996.Google Scholar
  35. [35]
    Y. Song and A. Zhang. Monotonic tree of images and its application in image processing. In technical report number 2001–12, CSE Department, State University of New York at Buffalo, August 2001.Google Scholar
  36. [36]
    M.J. Swain and D. Ballard. Color Indexing. Int Journal of Computer Vision, 7(1): 11–32, 1991.CrossRefGoogle Scholar
  37. [37]
    A.B. Torralba and A. Oliva. Semantic organization of scenes using discriminant structural templates. In International Conference on Computer Vision (ICCV99), pages 1253–1258, 1999.Google Scholar
  38. [38]
    C.T. Zahn. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans, on Computers, C-20:68–86, January 1971.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Yuqing Song
    • 1
  • Aidong Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

Personalised recommendations