Hypermedia Information Systems

  • Rajiv Khosla
  • Ernesto Damiani
  • William Grosky


In the last four chapters we have described applications of HCVM in e-sales recruitment, e-banking, e-business data organization and knowledge management. In chapter 5 we also described the multimedia component of the HCVM. In all these chapters multimedia has been looked at in terms of how it can be used for improving the representational efficiency, effectiveness and interpretation of computer-based artifacts and also to some extent how it can be used for perceptual problem solving. In fact, multimedia data (e.g., text, image, video and audio) today is an inherent part of Internet and web-based applications. In that respect, there are interesting research issues and problems associated with management and retrieval of multimedia data from multimedia databases. Queries and operations based on classical approaches (e.g., relational database structures) just won’t do for multimedia data, where browsing is an important paradigm. The importance of this paradigm is illustrated by the fact that multimedia databases are sometimes referred to as hypermedia databases. Standard indexing approaches won’t work for annotation independent, content-based queries over multimedia data. The problem is further compounded by the fact that metadata of different media artifacts cannot be effectively used for modeling user queries involving text, image, video and audio data. Incorporating user semantics is an effective way of dealing with multimedia data indexing and retrieval.


Image Retrieval Image Object Query Image Relevance Feedback Multimedia Data 
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. Ahmad, I. and Grosky, W.I. (1999). “Spatial Similarity-based Retrievals in Image Databases” in Journal of Computer Science and Information Management, 2, 1–10.Google Scholar
  2. Ang, Y.H., Li, Z. and Ong, S.H. (1995). “Image Retrieval Based on Multidimensional Feature Properties” in Storage and Retrieval for Image and Video Databases III, 2420, 47–57.Google Scholar
  3. Belongie, S., Carson, C, Greenspan, H. and Malik, J. (1998). “Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval” in Proceedings of the International Conference on Computer Vision, 675–682.Google Scholar
  4. Benitez, A.B., Beigi, M. and Chang, S.-F. (1998). “Using Relevance Feedback in Content-Based Image Metasearch” in IEEE Internet Computing, 2, 59–69.CrossRefGoogle Scholar
  5. Berry, M.W., Drmac, Z. and Jessup, E.R. (1998). “Matrices, Vector Spaces, and Information Retrieval”in SIAM Review, 2, 335–362.MathSciNetGoogle Scholar
  6. Bhanu, B., Peng, J. and Qing, S. (1998). “Learning Feature Relevance and Similarity Metrics in Image Databases” in Proceedings of the IEEE Workshop on Content–Based Access of Image and Video Libraries, 14–18.CrossRefGoogle Scholar
  7. Bigun, J. (1993). “Unsupervised Feature Reduction in Image Segmentation by Local Transforms” in Pattern Recognition Letters, 14, 573–583.CrossRefGoogle Scholar
  8. Chang, S.-F., Chen, W. and Sundaram, H. (1998). “Semantic Visual Templates: Linking Visual Features to Semantics” in Proceedings of the IEEE International Conference on Image Processing, 531–535.Google Scholar
  9. Colombo, C, Del Bimbo, A. and Pala, P. (1999). “Semantics in Visual Information Retrieval” in IEEE Multimedia, 6, 38–53.CrossRefGoogle Scholar
  10. Cox, I.J., Miller, M.L., Minka, T.P. and Yianilos, P.N. (1998). “An Optimized Interaction Strategy for Bayesian Relevance Feedback” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 553–558.Google Scholar
  11. Deerwester, S., Dumais, S.T. et. al. (1990). “Indexing by Latent Semantic Analysis” in Journal of the American Society for Information Science, 41,391–407.CrossRefGoogle Scholar
  12. Dimai, A. (1997). “Spatial Encoding Using Differences of Global Features” in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 352–360.Google Scholar
  13. Duygulu, P., Barnard, K., et. al. (2002). “Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary” in Seventh European Conference on Computer Vision, 97–112.Google Scholar
  14. Gibbs, S., Breiteneder, C. and Tsichritzis, D. (1997). “Modeling Time-Based Media” in The Handbook of Multimedia Information Management, W.I. Grosky, R. Jain, and R. Mehrotra (Eds.), Prentice Hall PTR, 13–38.Google Scholar
  15. Grosky, W.I. (1984). “Toward a Logical Data Model for Integrated Pictorial Databases” in Computer Vision, Graphics and Image Processing, 25, 371–382.CrossRefGoogle Scholar
  16. Grosky, W.I. (1994). “Multimedia Information Systems” in IEEE Multimedia, 1,12–24.CrossRefGoogle Scholar
  17. Grosky, W.I., Fotouhi, F. and Jiang, Z. (1998). “Using Metadata for the Intelligent Browsing of Structured Media Objects” in Managing Multimedia Data — Using Metadata to Integrate and Apply Digital Media, A. Sheth and W. Klas (Eds.), McGraw-Hill Publishing Company, 67–92.Google Scholar
  18. Gudivada V. and Raghavan, V.V. (1995). “Content-Based Image Retrieval Systems” in IEEE Computer, 28, 18–22.CrossRefGoogle Scholar
  19. Gupta, A., Weymouth, T. and Jain, R. (1991). “Semantic Queries with Pictures: The VIMSYS Model” in Proceedings of the 17 th International Conference on Very Large Databases,’ 69–79.Google Scholar
  20. Hsu, W., Chua, T.S. and Pung, H.K. (1995). “An Integrated Color-Spatial Approach to Content-based Image Retrieval” in Proceedings of ACM Multimedia, 305–313.Google Scholar
  21. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J. and Zabih, R. (1997). “Image Indexing Using Color Correlograms” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 762–768.Google Scholar
  22. Huang, J., Kumar, S.R. and Zabih, R. (1998). “An Automatic Hierarchical Image Classification Scheme” in Proceedings of the Sixth ACM International Conference on Multimedia, 219–228.CrossRefGoogle Scholar
  23. Jain, R. (1993). “NSF Workshop on Visual Information Management Systems” in Sigmod Record 23, 57–75.CrossRefGoogle Scholar
  24. Jagadish, H.V. (1991) “A Retrieval Technique for Similar Shapes” in Proceedings of the 1991 ACM SIGMOD Conference, Denver, 208–217.CrossRefGoogle Scholar
  25. Jain A.K. and Vailaya, A. (1996). “Image Retrieval Using Color and Shape” in Pattern Recognition, 29,1233–1244.CrossRefGoogle Scholar
  26. La Cascia, M., Sethi, S. and Sclaroff, S. (1998). “Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web” in Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries, 24–28.CrossRefGoogle Scholar
  27. Lu, G.-J. (1997). “An Approach to Image Retrieval Based on Shape” in Journal of Information Science, 23, 119–127.CrossRefGoogle Scholar
  28. Mokhtarian, F, Abbasi, S. and Kitter, J. (1996). “Efficient and Robust Retrieval by Shape Content through Curvature Scale Space” in Proceedings of International Workshop on Image Database and Multimedia Search, 35–42.Google Scholar
  29. Mehrotra, R. and Grosky, W.I. (1988). “SMITH: An Efficient Model-Based Two-Dimensional Shape Matching Technique” in Syntactic and Structural Pattern Recognition, G. Ferrate, T. Pavlidis, A. Sanfeliu, and H. Bunke (Eds.), Springer-Verlag, 233–248.CrossRefGoogle Scholar
  30. Mehrotra, R. and Gary, J.E. (1995). “Similar-Shape Retrieval in Shape Data Management” in IEEE Computer, 28, 57–62.CrossRefGoogle Scholar
  31. Meilhac, C. and Nastar, C. (1999). “Relevance Feedback and category Search in Image Databases” in Proceedings of the IEEE International Conference on Multimedia Computing and Systems, 512–517.CrossRefGoogle Scholar
  32. Minka, T.P. and Picard, R.W. (1997). “Interactive Learning with a Society of Models” in Pattern Recognition, 30, 565–581.CrossRefGoogle Scholar
  33. Mehtre, B.M., Kankanhalli, M.S., and Lee, W.-F. (1997). “Shape Measures for Content Based Image Retrieval: A Comparison” in Information Processing & Management, 33, 319–337.CrossRefGoogle Scholar
  34. Mehtre, B.M., Kankanhalli, M.S., and Lee, W.-F. (1998), “Content-Based Image Retrieval Using A Composite Color-Shape Approach” in Information Processing & Management, 34, 109–120.CrossRefGoogle Scholar
  35. Niblack, W., Barder, R. et. al. (1993). “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape” in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 1908, 173–181.Google Scholar
  36. Nievergelt, J., Hinterberger, H„ and Sevcik, K.C. (1984). “The Grid File: An Adaptable Symmetric Multikey File Structure” in ACM Transaction on Database Systems, 9,1984.Google Scholar
  37. Pass, G. and Zabih, R. (1996). “Histogram Refinement for Content-Based Image Retrieval” in IEEE Workshop on Applications of Computer Vision, 96–102.Google Scholar
  38. Pecenovic, Z. (1997). Image Retrieval Using Latent Semantic Indexing, Graduate Thesis, Department of Electrical Engineering, Swiss Federal Institute of Technology, Lausanne, Switzerland, June 1997.Google Scholar
  39. Pentland, A., Piccard, R.W., and Sclaroff, S. (1996). “Photobook: Content-Based manipulation of Image Databases” in International Journal of Computer Vision, 18, 233–254.CrossRefGoogle Scholar
  40. Rabitti F. and Stanchev, P. (1989). “GRM_DBMS: A Graphical Image DataBase System” in In Visual Database Systems, T. Kunii (Ed.), North-Holland Publishing Company, Amsterdam, The Netherlands, 415–430.Google Scholar
  41. Rabitti, F. and Savino, P. (1992). “Query Processing on Image Databases” in Visual Database Systems II, E. Knuth and L.M. Wegner (Eds.), North Holland Publishing Company, Amsterdam, 169–183.Google Scholar
  42. Rui, Y., Huang, T.S., Ortega, M., and Mehrotra, S. (1998). “Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval” in IEEE Transactions on Circuits and Systems for Video Technology, 8, 644–655.CrossRefGoogle Scholar
  43. Robinson, J.T. (1981). “K-D-B tree: A Search Structure for Large Multidimensional Dynamic Indices” in Proceedings of ACM SIGMOD Conference on the Management of Data, 1981.Google Scholar
  44. Santini, S. and Jain, R. (1996). “The Graphical Specification of Similarity Queries” in Journal of Visual Languages & Computing, 7, 403–421.CrossRefGoogle Scholar
  45. Santini, S. and Jain, R. (2000). “Integrated Browsing and Querying for Image Databases” in IEEE Multimedia, 7, 26–39.CrossRefGoogle Scholar
  46. Sethi, I.K., Coman, I., et. al. (1998). “Color-WISE: A System for Image Similarity Retrieval Using Color” in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 3312, 140–149.Google Scholar
  47. Smith, J.R. and Chang, S.-F. (1999). “Integrated Spatial and Point Feature Map Query,” in ACM Multimedia Systems Journal, 1, 129–140.CrossRefGoogle Scholar
  48. Sheikholeslami G., Chang, W., and Zhang, A. (1998). “Semantic Clustering and Querying on Heterogeneous Features for Visual Data” in Proceedings of the Sixth ACM International Conference on Multimedia, 3–12.CrossRefGoogle Scholar
  49. Strieker, M. and Dimai, A. (1996). “Color Indexing with Weak Spatial Constraints” in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 2670, 29–39.Google Scholar
  50. Stonebraker, M. (1996). Object-Relational DBMSs — The Next Great Wave, Morgan-Kaufmann Publishers, San Francisco, 1996.MATHGoogle Scholar
  51. Swain, M.J. and Ballard, D.H. (1991), “Color Indexing” in International Journal of Computer Vision,7, 11–32.CrossRefGoogle Scholar
  52. Tao, Y. and Grosky, W.I. (1999a). “Delaunay Triangulation for Image Object Indexing: A Novel Method for Shape Representation” in Proceedings of IS&T/SPIE’s Symposium on Storage and Retrieval for Image and Video Databases VII, pp. 631–642.Google Scholar
  53. Tao, Y. and Grosky, W.I. (1999b). “Object–Based Image Retrieval Using Point Feature Maps” in Proceedings of the 8th IFIP 2.6 Working Conference on Database Semantics, 59–73.Google Scholar
  54. Tamura, H. and Yokoya, N. (1984). “Image Database Systems: A Survey” in Pattern Recognition, 17, 29–43.CrossRefGoogle Scholar
  55. Taycher, L., La Cascia, M., and Sclaroff, S. (1997). “Image Digestion and Relevance Feedback in the ImageRover WWW Search Engine” in Proceedings of the International Conference on Visual Information, 85–92.Google Scholar
  56. Wan, X. and Kuo, C.-J. (1996). “Color Distribution Analysis and Quantization for Image Retrieval” in Proceedings of SPIE Storage and Retrieval for Image and Video Databases, 2670, 8–16.Google Scholar
  57. Widom, J. and Ceri, S. (1996). Active Database Systems — Triggers and Rules for Advanced Database Processing, Morgan Kaufmann Publishers, Inc., 1996.Google Scholar
  58. Zhao, R. and Grosky, W.I. (2002a). “Narrowing the Semantic Gap — Improved Text-Based Web Document Retrieval Using Visual Features” in IEEE Transactions on Multimedia, 4, 189–200.CrossRefGoogle Scholar
  59. Zhao, R. and Grosky, W.I. (2002b). “Negotiating the Semantic Gap: From Feature Maps to Semantic Landscapes” in Pattern Recognition, 35, 51–58.CrossRefGoogle Scholar
  60. Zhou, X.S. and Huang, T.S. (2002). “Unifying Keywords and Visual Contents in Image Retrieval” in IEEE Multimedia, 9,23–33.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Rajiv Khosla
    • 1
  • Ernesto Damiani
    • 2
  • William Grosky
    • 3
  1. 1.La Trobe UniversityAustralia
  2. 2.Universita di MilanoItaly
  3. 3.University of MichiganUSA

Personalised recommendations