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Discovering Patterns With and Within Images

  • Osmar R. Zaïane
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 22)

Abstract

The process of knowledge discovery from data, also known as KDD, comprises steps such as gathering and consolidating data, pre-processing then selecting data, mining the selected data, then finally evaluating the discovered patterns for possible interpretation and use [28]. To be profitable and constructive, this nontrivial process, which includes the step of data mining, needs to extract implicit, previously unknown and potentially useful information from large data [28]. The knowledge extracted, or discovered, is usually in the form of patterns such as data characterization, classes, clusters, frequent sequences, data models, rules such as association rules, etc. [15]. While knowledge discovery and data mining are typically used on corporate data for business intelligence, or on scientific data such as with bio-informatics, with the advances in multimedia data acquisition and storage techniques, the need for automatically discovering patterns from large image and video collections is becoming more and more relevant in many applications.

Keywords

Data Mining Association Rule Knowledge Discovery Visual Feature Image Query 
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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References

  1. [1]
    R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proc. Int. Conf. Very Large Data Bases, pp. 487–499, Santiago, Chile, September, 1994Google Scholar
  2. [2]
    P. Aigrain, H. Zhang and D. Petkovic, Content-Based Representation and Retrieval of Visual Media: A State-of-the-Art Review, Multimedia Tools and Applications: an International Journal, vol. 3, n.3, pp. 179–202, November, 1996CrossRefGoogle Scholar
  3. [3]
    M.L. Antonie, O. R. Zaïane, A. Coman, Application of Data Mining Techniques for Medical Image Classification, in Proc. of Second Intl. Workshop on Multimedia Data Mining (MDM/KDD′2001) in conjunction with Seventh ACM SIGKDD, pp. 94–101, San Francisco, CA, August 26, 2001.Google Scholar
  4. [4]
    J.R. Bach, C. Fuller, A. Gupta et al., The Virage Image Search Engine: An open framework for image management, SPIE Storage and Retrieval for Image and Video Databases IV, February, 1996Google Scholar
  5. [5]
    R. Beckwith, C. Fellbaum, D. Gross, K. Miller, G.A. Miller and R. Tengi, Five Papers on WordNet, Special Issue of Journal of Lexicography, vol. 3, n. 4, pp. 235–312, 1990 Available at: ftp://ftp.cogsci.princeton.edu Google Scholar
  6. [6]
    J.A. Blackard and D.J. Dean, Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables, Computers and Electronics in Agriculture, pp. 131–152, vol. 24, n. 3, 1999CrossRefGoogle Scholar
  7. [7]
    C.E. Brodley, A. Kak, J. Dy, C. R. Shyu, A. Aisen and L. Broderick, Content-Based Retrieval from Medical Image Database: A synergy of human interaction, Machine Learning and Computer Vision, Proc. 16th National Conference on Artificial Intelligence, pp. 760–767, Orlando, Florida, USA, July, 1999Google Scholar
  8. [8]
    M.C. Burl, C. Fowlkes, J. Roden, Mining for Image Content, In Systemics, Cybernetics, and Informatics/Information Systems: Analysis and Synthesis, Orlando, FL, July 1999Google Scholar
  9. [9]
    M.C. Burl, D. Lucchetti, Autonomous Visual Discovery, Data Mining and Knowledge Discovery: Theory, Tools and Technology II, pp. 240–248, Orlando, April, 2000Google Scholar
  10. [10]
    T.Caelli, L. Cheng, V. Bulitko, private communication, integrated Natural Resources Inventory & Information System, http://www.arc.ab.ca/extranet/forres/inriis/
  11. [11]
    C. Carson, M. Thomas, S. Belongie, J. M. Hellerstein and J. Malik, Blobworld: A System for Region-Based Image Indexing and Retrieval, International Conference on Visual Information Systems, 1999Google Scholar
  12. [12]
    I. Christoyianni, E. Dermatas, G. Kokkinakis, Fast Detection of Masses in Digitized Mammograms, IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 2355–2358, Phoenix, Arizona, USA, March 1999.Google Scholar
  13. [13]
    U. Fayyad and P. Smyth, Image Database Exploration: Progress and Challenges, Proc. Knowledge Discovery in Databases Workshop, pp. 14–27, Washington, D.C, 1993Google Scholar
  14. [14]
    U. M. Fayyad, S. G. Djorgovski and N. Weir, Automating the Analysis and Cataloguing of Sky Surveys, in Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth and R. Uthurusamy (Editors), pp. 471–493, AAAI/MIT Press, 1996Google Scholar
  15. [15]
    U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (eds.), Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996Google Scholar
  16. [16]
    M. Flickner, H. Sawhney, W. Niblack et al., Query by Image and Video Content: The QBIC System, IEEE Computer, vol. 28, n. 9, pp. 23–32, September, 1995CrossRefGoogle Scholar
  17. [17]
    D. Forsyth, J. Malik, M. Flek, H. Greenspan, T. Leung, S. Belongie, C. Carson and C. Bregler, Finding pictures of objects in large collections of images, Technical report, CS Department, University of California Berkeley, 1997Google Scholar
  18. [18]
    C. Frankel, M. Swain and V. Athitsos, Webseer: An image search engine for the World Wide Web, TR-96-14, CS Department, Univ. of Chicago, November, 1996Google Scholar
  19. [19]
    W. Hsu, M.L. Lee, B. Liu and T. W. Ling, Exploration Mining in Diabetic Patient Databases: Findings and Conclusions, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 20 Aug 2000, Boston, U.S.A., pp. 430–436Google Scholar
  20. [20]
    M. Larsen and M. Rudemo, Using ray-traced templates to find individual trees in aerial photos, Proc. 10th Scandinavian Conference on Image Analysis, vol. 2, pp. 1007–1014, Lappeenranta, Finland, 1997Google Scholar
  21. [21]
    Z.N. Li, O. R. Zaïane and B. Yan, C-BIRD: Content-Based Image Retrieval in Digital Libraries Using Illumination Invariance and Recognition Kernel, Proc. International Workshop on Storage and Retrieval Issues in Image and Multimedia Databases, in 9th International Conference on Database and Expert Systems (DEXA), Vienna, Austria, August, 1998Google Scholar
  22. [22]
    B. Lovell, Quality Control in Cell Nucleus Segmentation, Medical Image Processing and Computer Projects at the University of Queensland, Australia, private communication, 2000Google Scholar
  23. [23]
    M. T. Maybury (editor), Intelligent Multimedia Information Retrieval, AAAI/MIT Press, 1997Google Scholar
  24. [24]
    S. Mukherjea, K. Hirata and Y. Hara, Towards a Multimedia World Wide Web Information Retrieval Engine, Proc. Sixth International WWW Conference, Santa Clara, CA, USA, 1997Google Scholar
  25. [25]
    C. Ordonez and E. Omiecinski, Discovering Association Rules based on Image Content, Proc. IEEE Forum on Research and technology Advances in Digital Libraries, Baltimore, Maryland, USA, May, 1999Google Scholar
  26. [26]
    V. Oria, M. T. Özsu, B. Xu, L. I. Cheng and P.J. Iglinski, VisualMOQL: The DISIMA Visual Query Language, Proc. of the 6th IEEE International Conference on Multimedia Computing and Systems, vol. 1, pp. 536–542, Florence, Italy, June 1999Google Scholar
  27. [27]
    A. Pentland, R.W. Picard and S. Sclaroff, Photobook: Tools for Content-Based Manipulation of Image Databases, SPIE Storage and Retrieval for Image and Video Databases II, vol. 2, n. 185, pp. 34–47, 1994Google Scholar
  28. [28]
    G. Piatetsky-Shapiro, U. Fayyad and P. Smith, From Data Mining to Knowledge Discovery: An Overview, in Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy editors, pp. 1–35, AAAI/MIT Press, 1996Google Scholar
  29. [29]
    A. P. Sistla and C. Yu, Similarity-Based Retrieval of Pictures using Indices on Spatial Relationships, Proc 21st Conf. on Very Large Data Bases (VLDB′95), 1995Google Scholar
  30. [30]
    J.R. Smith and S.F. Chang, Visually searching the web for content, IEEE Multimedia, vol.4, n. 3, pp. 12–20, 1997CrossRefGoogle Scholar
  31. [31]
    O. R. Zaïane, J. Han, Z.N. Li, J. Y. Chiang and S. Chee, MultiMedia-Miner: A System Prototype for Multimedia Data Mining, Proc. 1998 ACM-SIGMOD Conf. on Management of Data, pp. 581–583, Seattle, Washington, June, 1998Google Scholar
  32. [32]
    O. R. Zaïane, J. Han, Z.N. Li and J. Hou, Mining Multimedia Data, Proc. CASCON′98: Meeting of Minds, pp. 83–96, Toronto, Canada, November, 1998Google Scholar
  33. [33]
    O. R. Zaïane, Resource and Knowledge Discovery from the Internet and Multimedia Repositories, PhD thesis, School of Computing Science, Simon Fraser University, Canada, 1999Google Scholar
  34. [34]
    O. R. Zaïane, E. Hagen and J. Han, Word Taxonomy for On-line Visual Asset Management and Mining, Proc. 4th International Workshop on Application of Natural Language to Information Systems (NLDB), Klagenfurt, Austria, June, 1999Google Scholar
  35. [35]
    O. R. Zaïane, J. Han, H. Zhu, Mining Recurrent Items in Multimedia with Progressive Resolution Refinement, Proc. International Conference on Data Engineering (ICDE′2000), pp. 461–470, San Diego, CA, February, 2000Google Scholar
  36. [36]
    O. R. Zaïane, Mining Multimedia Data, Invited talk, XIV Brazilian Symposium on Databases (SBBD′2000), João Pessoa, Paraíba, Brazil, October, 2000Google Scholar
  37. [37]
    T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: an efficient data clustering method for very large databases, SIGMOD′96, pp. 103–114, Montreal, Canada, June 1996.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Osmar R. Zaïane
    • 1
  1. 1.University of AlbertaCanada

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