An extended object-oriented data model for large image bases

  • Amarnath Gupta
  • Terry E. Weymouth
  • Ramesh Jain
Meta-Knowledge And Data Models
Part of the Lecture Notes in Computer Science book series (LNCS, volume 525)


This paper presents an object-oriented data model for an image database. The formal presentation of the model stems from an analysis of the domain of remotely sensed radar images of the Arctic ice. The data model maintains the distinction between generic spatial attributes and representation dependent spatial attributes. This results in a four-layer network of objects, attributes, relations, events and representations. In the representation level, the data model is very close to a functional data model. The model serves as the formal foundation of the VIMSYS (Visual Information Management SYStem) project, which aims to perform content and similarity based query processing of large image repositories.


Data Model Query Processing Image Database Image Object Synthetic Aperture Radar Image 
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. [Bator 88]
    D.S. Batory, T.Y. Leung and T. Wise, “Implementation Concepts for an Extensible Data Model and Data Language”, ACM Trans. Data. Sys, Vol. 13, No. 3, pp. 231–262, 1988.Google Scholar
  2. [Brol 89]
    J. Brolio et al, “ISR: A Database for Symbolic Processing in Computer Vision”, IEEE Computer, pp. 22–30, Dec. 1989.Google Scholar
  3. [Buch 90]
    A. Buchman et al (eds.), “Design and Implementation of Large Spatial Databases”, Lecture Notes in Comp. Sc. 409, Springer-Verlag, 1990.Google Scholar
  4. [Card 89]
    A.F. Cardenas (ed.) Semantic and Object-Oriented Database Systems, Prentice Hall, 1989.Google Scholar
  5. [Chang 81]
    N.S. Chang and K.S. Fu, “Picture Query Languages for Pictorial Data-Base Systems”, IEEE Computer, pp. 23–33, Nov. 1981.Google Scholar
  6. [Chang 89]
    S.K. Chang, Principles of Pictorial Information Systems Design, Prentice Hall, 1989.Google Scholar
  7. [Daida 90]
    J. Daida, R. Samadani and J.F. Vesecky, “Object-Oriented Feature Tracking Algorithms for SAR Images of the Marginal Ice Zone”, IEEE Trans. Geosc. Remote Sens., Vol. 28, No. 4, pp. 573–589, 1990.Google Scholar
  8. [Elma 90]
    R. Elmasri, G.T. Wuu and Y.G. Kim, “The Time Index-An Access Structure for Temporal Data”, 16th Int. Conf. VLDB, pp. 1–12, 1990.Google Scholar
  9. [Fily 87]
    M. Fily and D.A. Rothrock, “Sea Ice Tracking by Nested Correlation”, IEEE Trans. Geosc. Remote Sens., Vol. 25, No. 5, pp. 570–580, 1987.Google Scholar
  10. [Fu 82]
    K.S. Fu and T.L. Kunii (eds.) Picture Engineering, Springer Verlag, 1982.Google Scholar
  11. [Gadia 88]
    S. Gadia, “A Homogeneous Relational Model and Query Language gor Temporal Databases”, ACM TODS, Vol. 13, No. 4, pp. 418–448, 1988.Google Scholar
  12. [Gros 84]
    W.I. Grosky, “A Logical Data Model for Integrated Pictorial Databases”, Comput. Vis. Graph. and Image. Proc., Vol. 25 No. 3, pp. 371–382, 1984.Google Scholar
  13. [Gros 90]
    W.I. Grosky and R. Mehrotra, “Index-Based Object Recognition in Pictorial Data Management”, Comput. Vis. Graph. and Image. Proc., Vol. 52 No. 3, pp. 416–436, 1990.Google Scholar
  14. [Gupta 91]
    A. Gupta, T.E. Weymouth and R. Jain, “Semantic Queries with Pictures: the VIMSYS Model”, to appear in 17th Int. Conf. on VLDB, 1991.Google Scholar
  15. [Gutin 89]
    R.H. Guting, “Gral: An Extensible Relational Database System for Geometric Applications”, Proc. 15th Int. Conf. VLDB, pp. 33–44, 1989.Google Scholar
  16. [Hara 90]
    L. Harada, M. Nakano, M. Kitsuregawra and M. Takagi, “Query Processing Method for Multi-Attribute Clustered Relations”, 16th Int. Conf. VLDB, pp. 59–70, 1990.Google Scholar
  17. [Henri 89]
    A. Henrich, H-W Six, P. Widmayer, “The LSD Tree: Spatial Access to Multidimensional Point and Non-point Objects”, 15th Int. Conf. VLDB, pp. 45–53, 1989.Google Scholar
  18. [Jenq 90]
    B.P. Jenq, D. Woelk, W. Kim and W.L. Lee, “Query Processing in Distributed ORION”, Proc. Int. Conf. on EBDT, pp. 169–187, 1990.Google Scholar
  19. [Josep 88]
    T. Joeseph and A.F. Cardenas, “PICQUERY: A High-level Query Language for Pictorial Database Management”, IEEE Trans. Soft. Eng., Vol. 14, No. 5, pp. 630–638, 1988.Google Scholar
  20. [Khosh 90]
    S. Khoshafian, “Insight into Object-oriented Databases”, Info. and Soft. Techno., Vol. 32, No. 4, pp. 274–289, 1990.Google Scholar
  21. [Kim 89]
    W. Kim, “A Model of Queries in Object-Oriented Databases”, Proc. 15th Int. Conf. VLDB, pp. 423–432, 1989.Google Scholar
  22. [Kim 90]
    W. Kim, ”Object Oriented databases: Research Directions”, IEEE Trans. Knowl and Data Eng., Vol. 2, No. 3, pp. 327–341, 1990.Google Scholar
  23. [Leclu 88]
    C. Lecluse, P. Richard and F. Velz, “O2, An Object-Oriented Data Model”, Proc. SIGMOD, 1988.Google Scholar
  24. [Leong 89]
    M.K. Leong, S. Sam and D. Narsimhalu, “Towards a Visual Language for an ObjectOriented Multi-Media Database System”, in Visual Database Systems, T.L. Kunii (ed.), Elsivier Sc. Pub., 1989.Google Scholar
  25. [Oren 88]
    J.A. Orenstein and F.A. Manola, “PROBE: Spatial Data Modeling and Query Processing in an Image Database App.lication”, IEEE Trans. on Soft. Engin., Vol. 14, No. 5, pp. 611–629, May 1988.Google Scholar
  26. [Rabit 89]
    F. Rabitti and P. Stanchev, “GRIM_DBMS: A Graphical Image Database Management System”, in Visual Database Systems, T.L. Kunii (ed.), Elsivier Sc. Pub., 1989.Google Scholar
  27. [Rao 90]
    A.R. Rao, A Taxonomy for Texture Description and Identification, Springer Verlag, 1990.Google Scholar
  28. [Rosso 88]
    N. Rossopoulos, C. Falotsos and T. Sellis, ”An effificient Pictorial Database System for PSQL”, IEEE Trans. on Soft. Engin., Vol. 14, No. 5, pp. 639–650, 1988.Google Scholar
  29. [Samad 87]
    R. Samadani, “Image Pyramid Motion Detection Applied to Sea Ice”, Ph.D. Dissertation, Stanford Univ., 1987.Google Scholar
  30. [Seeg 90]
    B. Seeger and H-P. Kriegel, “The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems”, 16th Int. Conf. VLDB, pp. 590–601, 1990.Google Scholar
  31. [Shap 85]
    L.G. Shapiro and R.M. Haralick, “A Metric for Comparing Relational Descriptions”, IEEE Trans. on PAMI, Vol. 7, No. 1, pp. 90–94, 1985.Google Scholar
  32. [Ship 81]
    D.W. Shipman, “The Functional Data Model and the Data Language DAPLEX”, ACM Trans. Data. Sys, Vol. 6, No. 1, pp. 140–173, 1981.Google Scholar
  33. [Shu 90]
    C. Shu, R.C. Jain and F. Quek, “A Linear Algorithm for Computing the Phase Portraits of Oriented Texture”, submitted to CVPR 91.Google Scholar
  34. [Tamu 84]
    H. Tamura and N. Yokoya, “Image Database Systems: A Survey”, Pattern Recog., Vol. 17, No. 1, pp. 29–43, 1984.Google Scholar
  35. [Tourn 87]
    J. Tournadre and C. Gautier, “Automatic Cloud Field Analysis Based on Spectral and Textural Signature”, in Remote Sensing Research Methods '87, A. Deepak, H.E. Fleming and J.S. Theon (eds.), A. Deepak Pub., 1989.Google Scholar
  36. [Wang 90]
    T-L. Wang and D. Shasha, “Query Processing for Distance Metrics”, 16th Int. Conf. VLDB, pp. 602–612, 1990.Google Scholar
  37. [Wilso 88]
    R. Wilson and M. Spann, Image Segmentation and Uncertainty, Letchworth: Research Studies Press, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Amarnath Gupta
    • 1
  • Terry E. Weymouth
    • 1
  • Ramesh Jain
    • 1
  1. 1.Artificial Intelligence LaboratoryUniversity of MichiganAnn Arbor

Personalised recommendations