Automatic content modeling and retrieval in remote sensing image databases are important and challenging problems. Statistical pattern recognition and computer vision algorithms concentrate on feature-based analysis and representations in pixel or region levels whereas syntactic and structural techniques focus on modeling symbolic representations for interpreting scenes. We describe a hybrid hierarchical approach for image content modeling and retrieval. First, scenes are decomposed into regions using pixel-based classifiers and an iterative split-and-merge algorithm. Next, spatial relationships of regions are computed using boundary, distance and orientation information based on different region representations. Finally, scenes are modeled using attributed relational graphs that combine region class information and spatial arrangements. We demonstrate the effectiveness of this approach in query scenarios that cannot be expressed by traditional approaches but where the proposed models can capture both feature and spatial characteristics of scenes and can retrieve similar areas according to their high-level semantic content.


Spatial Relationship Relational Graph Region Pair Remote Sensing Image Scene Modeling 
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.


  1. 1.
    Dance, S., Caelli, T., Liu, Z.Q.: Picture Interpretation: A Symbolic Approach. World Scientific, Singapore (1995)MATHCrossRefGoogle Scholar
  2. 2.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 18, 265–298 (2004)CrossRefGoogle Scholar
  3. 3.
    Berretti, S., Bimbo, A.D., Vicario, E.: Modelling spatial relationships between colour clusters. Pattern Analysis & Applications 4, 83–92 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Smith, J.R., Chang, S.F.: VisualSEEk: A fully automated content-based image query system. In: Proceedings of ACM International Conference on Multimedia, Boston, MA, pp. 87–98 (1996)Google Scholar
  5. 5.
    Chu, W.W., Hsu, C.C., Cardenas, A.F., Taira, R.K.: Knowledge-based image retrieval with spatial and temporal constructs. IEEE Transactions on Knowledge and Data Engineering 10, 872–888 (1998)CrossRefGoogle Scholar
  6. 6.
    Tang, H.L., Hanka, R., Ip, H.H.S.: Histological image retrieval based on semantic content analysis. IEEE Transactions on Information Technology in Biomedicine 7, 26–36 (2003)CrossRefGoogle Scholar
  7. 7.
    Petrakis, E.G.M., Faloutsos, C., Lin, K.I.: Imagemap: An image indexing method based on spatial similarity. IEEE Transactions on Knowledge and Data Engineering 14, 979–987 (2002)CrossRefGoogle Scholar
  8. 8.
    Aksoy, S., Tusk, C., Koperski, K., Marchisio, G.: Scene modeling and image mining with a visual grammar. In: Chen, C.H. (ed.) Frontiers of Remote Sensing Information Processing, pp. 35–62. World Scientific, Singapore (2003)CrossRefGoogle Scholar
  9. 9.
    Aksoy, S., Koperski, K., Tusk, C., Marchisio, G., Tilton, J.C.: Learning Bayesian classifiers for scene classification with a visual grammar. IEEE Transactions on Geoscience and Remote Sensing 43, 581–589 (2005)CrossRefGoogle Scholar
  10. 10.
    Ballard, D.H., Brown, C.M.: Computer Vision. Prentice-Hall, Englewood Cliffs (1982)Google Scholar
  11. 11.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)MATHCrossRefGoogle Scholar
  12. 12.
    Christmas, W.J., Kittler, J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 749–764 (1995)CrossRefGoogle Scholar
  13. 13.
    Messmer, B.T., Bunke, H.: Efficient subgraph isomorphism detection: A decomposition approach. IEEE Transactions on Knowledge and Data Engineering 12, 307–323 (2000)CrossRefGoogle Scholar
  14. 14.
    Myers, R., Wilson, R.C., Hancock, E.R.: Bayesian graph edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 628–635 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Selim Aksoy
    • 1
  1. 1.Department of Computer EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations