Pattern recognition methods in image and video databases: Past, present and future

  • Sameer Antani
  • Rangachar Kasturi
  • Ramesh Jain
Invited Talks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

Image and video (multimedia) database systems have been on an increase in recent years. Several applications demand the retrieval of multimedia data from these database systems based on their content. The users of these systems and applications perceive the data in different ways and demand the ability to query the data based on their perception of the content. This need has spurred an interest to develop pattern recognition methods which can capture the visual information content and place them in a suitable form for database indexing. This paper describes some of the image and video database systems and the various pattern recognition methods used therein.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    G. Ahanger and T. D. C. Little. A Survey of Technologies for Parsing and Indexing Digital Video. Journal of Visual Communication and Image Representation, special issue on Digital Libraries, 7(1):28–43, 1996.Google Scholar
  2. 2.
    A. Akutsu et al. Video Indexing using Motion Vectors. In Proceedings of SPIE Visual Communications and Image Processing, volume 1818, pages 1522–1530, 1992.Google Scholar
  3. 3.
    J. Ashley, R. Barber, M. D. Flickner, J. L. Hafner, D. Lee, W. Niblack, and D. Petkovic. Automatic and semiautomatic methods for image annotation and retrieval in QBIC. Proceedings of IS&9T/SPIE Conference on Storage and Retrieval for Image and Video Databases III, Vol. SPIE 2420, pages 24–35, 1995.Google Scholar
  4. 4.
    M. Borchani and G. Stammon. Use of texture features for image classification and retrieval. In Proceedings of IS&T/SPIE Conference on Multimedia Storage and Archiving Systems II, Vol. SPIE 3229, pages 401–406, 1997.Google Scholar
  5. 5.
    N.-S. Chang and K.-S. Fu. Query-by-pictorial-example. IEEE Transactions on Software Engineering, 6(6):519–524, 1980.Google Scholar
  6. 6.
    A. Del Bimbo, M. Mugnaini, P. Pala, and F. Turco. PICASSO: Visual querying by color perceptive regions. In Second International Conference on Visual Information Systems (VISUAL'97), pages 125–131, 1997.Google Scholar
  7. 7.
    A. DelBimbo and P. Pala. Effective image retrieval using deformable templates. In Proc. International Conference on Pattern Recognition, pages 120–124, 1996.Google Scholar
  8. 8.
    M. Flickner, Sawhney H., W. Niblack, et al. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23–31, 1995.Google Scholar
  9. 9.
    T. Gevers. Color Image Invariant Segmentation And Retrieval. PhD thesis, Department of WINS, University of Amsterdam, 1996.Google Scholar
  10. 10.
    T. Gevers and A. W. M. Smeulders. Pictoseek: A content-based image search system for the world wide web. In Second International Conference on Visual Information Systems (VISUAL'97), pages 93–100, 1997.Google Scholar
  11. 11.
    C. Goble, M. O'Docherty, P. Crowther, M. Ireton, J. Oakley, and C. Xydeas. The manchester multimedia information system. Proc. E. D. B. T.'92 Conf. on Advances in Database Technology, 580:39–55, 1994.Google Scholar
  12. 12.
    Y. Gong. Intelligent Image Databases — Towards Advanced Image Retrieval. Kluwer Academic Publishers, Boston, 1998.Google Scholar
  13. 13.
    V. N. Gudivada and V. V. Raghavan. Content-based image retrieval systems. IEEE Computer, 28(9):18–22, 1995.Google Scholar
  14. 14.
    A. Gupta and R. Jain. Visual information retrieval. Communications of the ACM, 40(5):70–79, May 1997.CrossRefGoogle Scholar
  15. 15.
    A. Gupta, S. Santini, and R. Jain. In search of information in visual media. Communications of the ACM, 40(12):34–42, 1997.CrossRefGoogle Scholar
  16. 16.
    A. Hampapur, R. Jain, and T. Weymouth. Production Model based Digital Video Segmentation. Journal of Multimedia Tools and Applications, 1(1):9–46, 1995.CrossRefGoogle Scholar
  17. 17.
    J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih. Image indexing using color correlograms. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 762–768, 1997.Google Scholar
  18. 18.
    F. Idris and S. Panchanathan. Image and video indexing using vector quantization. Machine Vision and Applications, 10:43–50, 1997.CrossRefGoogle Scholar
  19. 19.
    A. K. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8):1233–1244, 1996.CrossRefGoogle Scholar
  20. 20.
    R. Kurniawati, J. S. Jin, and J. A. Sheperd. The SS +-tree: An improved index structure for similarity searches in a high-dimensional feature space. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases V, Vol. SPIE 3022, pages 110–120, 1997.Google Scholar
  21. 21.
    Z. Lei, T. Tasdizen, and D. Cooper. Object signature curve and invariant shape patches for geometric indexing into pictcfial databases. In Proceedings of IS&T/SPIE Conference on Multimedia Storage and Archiving Systems II, Vol. SPIE 3229, pages 232–243, 1997.Google Scholar
  22. 22.
    M. S. Lew, D. P. Huijsmans, and D. Denteneer. Content based image retrieval: KLT, projections, or templates. In Proc. of the First International Workshop on Image Databases and Multi-Media Search, pages 27–34, 1996.Google Scholar
  23. 23.
    R. Lienhart, W. Effelsberg, and Jain R. VisuaIGREP: A systematic method to compare and retrieve video sequences. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases VI, Vol. SPIE 3312, pages 271–282, 1997.Google Scholar
  24. 24.
    H. C. Lin, L. L. Wang, and S. N. Yang. Color image retrieval based on hidden markov-models. IEEE Transactions on Image Processing, 6(2):332–339, 1997.CrossRefGoogle Scholar
  25. 25.
    F. Liu and R. W. Picard. Periodicity, directionality, and randomness: Wold features for image modeling and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7):722–733, 1996.CrossRefGoogle Scholar
  26. 26.
    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, 1996.CrossRefGoogle Scholar
  27. 27.
    T. P. Minka and R. W. Picard. Interactive learning with a society of models. Pattern Recognition, 30(4):565–581, 1997.CrossRefGoogle Scholar
  28. 28.
    W. Niblack, X. Zhu, J. L. Hafner, T. Breuel, et al. Updates to the QBIC system. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases VI, Vol. SPIE 3312, pages 150–161, 1997.Google Scholar
  29. 29.
    R. Pass, G. Zabih and J. Miller. Comparing images using color coherence vectors. In 4th ACM Conference on Multimedia, 1996.Google Scholar
  30. 30.
    N. V. Patel and I. K. Sethi. Video shot detection and characterization for video databases. In To appear in Pattern Recognition, Special Issue on Multimedia, 1997.Google Scholar
  31. 31.
    A. Pentland, R. W. Picard, and S. Scarloff. Photobook: Tools for content-based manipulation of image databases. In Proceedings of IS&/SPIE 23rd IAPR Workshop on Image and Information Systems, Vol. SPIE 2368, pages 37–50, 1994.Google Scholar
  32. 32.
    A. P. Pentland, R. W. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. International Journal of Computer Vision, 18(3):233–254, 1996.CrossRefGoogle Scholar
  33. 33.
    E. G. M. Petrakis and S. C. Orphanoudakis. Methodology for the representation, indexing and retrieval of images by content. Image and Vision Computing, 11(8):504–521, 1993.CrossRefGoogle Scholar
  34. 34.
    T. Pun and D. Squire. Statistical structuring of pictorial databases for content-based image retrieval-systems. Pattern Recognition Letters, 17(12):1299–1310, 1996.CrossRefGoogle Scholar
  35. 35.
    J. Puzicha, T. Hofmann, and J. M. Buhmann. Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 267–272, 1997.Google Scholar
  36. 36.
    Y. Rui, T. S. Huang, S. Mehrotra, and M. Ortega. Automatic matching tool selection using relevance feedback in MARS. In Second International Conference on Visual Information Systems (VISUAL'97), pages 109–116, 1997.Google Scholar
  37. 37.
    H. Samet and A. Soffer. MARCO: MAp Retrieval by Content. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8):783–798, 1996.CrossRefGoogle Scholar
  38. 38.
    S. Santini and R. Gupta. Similarity queries in image databases. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1996.Google Scholar
  39. 39.
    S. Santini and R. Jain. Similarity matching. Personal communications with the authors.Google Scholar
  40. 40.
    S. Scarloff. Deformable prototypes for encoding shape categories in image databases. Pattern Recognition, 30(4):627–641, 1997.CrossRefGoogle Scholar
  41. 41.
    I. K. Sethi and N. V. Patel. A Statistical Approach to Scene Change Detection. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases III, Vol. SPIE 2420, 1995.Google Scholar
  42. 42.
    B. Shahraray. Scene Change Detection and Content-based Sampling of Video Sequences. In SPIE/IS&T Symposium on Electronic Imaging Science and Technology: Digital Video Compression: Algorithms and Technologies, volume 2419, 1995.Google Scholar
  43. 43.
    J. R. Smith and Chang. S-F. Quad-tree segmentation for texture-based image query. In ACM International Conference on Multimedia, pages 279–286, 1994.Google Scholar
  44. 44.
    M. A. Smith and T. Kanade. Video Skimming for Quick Browsing based on Audio and Image Characterization. Technical Report CMU-CS-95-186, Carnegie Mellon University, 1995.Google Scholar
  45. 45.
    A. Soffer. Image categorization using N x M-grams. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases V, Vol. SPIE 3022, pages 121–132, 1997.Google Scholar
  46. 46.
    M. Stricker. Bounds of the discrimination power of colorindexing techniques. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases II, Vol. SPIE 2185, pages 15–24, 1994.Google Scholar
  47. 47.
    M. Stricker and A. Dimai. Spectral covariance and fuzzy regions for image indexing. Machine Vision and Applications, 10(2):66–73, 1997.CrossRefGoogle Scholar
  48. 48.
    M. Stricker and M. Orengo. Similarity of color images. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases III, Vol. SPIE 2420, pages 381–392, 1995.Google Scholar
  49. 49.
    N. Strobel, C. S. Li, and V. Castelli. MMAP: Modified Maximum a Posteriori algorithm for image segmentation in large image/video databases. In Proc. IEEE International Conference on Image Processing, pages 196–199, 1997.Google Scholar
  50. 50.
    M. J. Swain and D. H. Ballard. Color Indexing. International Journal of Computer Vision, 7(1):11–32, 1991.CrossRefGoogle Scholar
  51. 51.
    D. White and Jain R. Similarity indexing: Algorithms and performance. In Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases IV Vol. SPIE 2670, pages 62–73, 1996.Google Scholar
  52. 52.
    B. Yeo and B. Liu. Rapid Scene Analysis on Compressed Video. IEEE Transactions on Circuits and Systems for Video technology, vn5 (6), 1995.Google Scholar
  53. 53.
    R. Zabih, R. Miller, and K. Mai. A feature-based algorithm for detecting and classifying scene breaks. In ACM International Conference on Multimedia, pages 189–200, 1995.Google Scholar

Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Sameer Antani
    • 1
  • Rangachar Kasturi
    • 1
  • Ramesh Jain
    • 2
  1. 1.Department of Computer Science and EngineeringThe Pennsylvania State UniversityUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of California at San DiegoLa JollaUSA

Personalised recommendations