Multi-sensor Information Fusion by Query Refinement

  • Shi-Kuo Chang 
  • Gennaro Costagliola
  • Erland Jungert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2314)


To support the retrieval and fusion of multimedia information from multiple real-time sources and databases, a novel approach for sensor-based query processing is described. The sensor dependency tree is used to facilitate query optimization. Through query refinement one or more sensor may provide feedback information to the other sensors. The approach is also applicable to evolutionary queries that change in time and/or space, depending upon the temporal/spatial coordinates of the query originator.


Query Processing Mobile Agent Information Fusion Query Optimization Certainty Factor 
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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  1. 1.
    J. Baumann et al., “Mole — Concepts of a Mobile Agent System”, World Wide Web, Vol. 1, No. 3, 1998, pp 123–137.CrossRefGoogle Scholar
  2. 2.
    C. Baumer, “Grasshopper — A Universal Agent Platform based on MASIF and FIPA Standards”, First International Workshop on Mobile Agents for Telecommunication Applications (MATA’99), Ottawa, Canada, October 1999, World Scientific, pp 1–18.Google Scholar
  3. 3.
    K. Chakrabarti, K. Porkaew and S. Mehrotra, “Efficient Query Refinement in Multimedia Databases”, 16th International Conference on Data Engineering, San Diego, California, February 28–March 3, 2000.Google Scholar
  4. 4.
    S. K. Chang and E. Jungert, Symbolic Projection for Image Information Retrieval and Spatial Reasoning, Academic Press, London, 1996.Google Scholar
  5. 5.
    S. K. Chang and E. Jungert, “A Spatial/temporal query language for multiple data sources in a heterogeneous information system environment”, The International Journal of Cooperative Information Systems (IJCIS), vol. 7, Nos 2 & 3, 1998, pp 167–186.CrossRefGoogle Scholar
  6. 6.
    S. K. Chang, G. Costagliola and E. Jungert, “Querying Multimedia Data Sources and Databases”, Proceedings of the 3rd International Conference on Visual Information Systems (Visual’99), Amsterdam, The Netherlands, June 2–4, 1999.Google Scholar
  7. 7.
    S. K. Chang, “The Sentient Map”, Journal of Visual Languages and Computing, Vol. 11, No. 4, August 2000, pp 455–474.CrossRefGoogle Scholar
  8. 8.
    S. K. Chang, T. H. Chen and C. S. Li, “Gesture-Enhanced Information Retrieval and Presentation in a Distributed Learning Environment”, Proceedings of the International Conference on Multimedia (ICME’2000), New York, July 31 to August 2, 2000.Google Scholar
  9. 9.
    S. K. Chang, G. Costagliola and E. Jungert, “Spatial/Temporal Query Processing for Information Fusion Applications”, Proceedings of the 4th International Conference on Visual Information Systems (Visual’2000), Lyon, France, November 2000, Lecture Notes in Computer Sciences 1929, Robert Laurini (Ed.), Springer, Berlin, pp 127–139.Google Scholar
  10. 10.
    C.-Y. Chong, S. Mori, K.-C Chang and W. H. Baker, “Architectures and Algorithms for Track Association and Fusion”, Proceedings of Fusion’99, Sunnyvale, CA, July 6–8, 1999, pp 239–246.Google Scholar
  11. 11.
    C. Date, An Introduction to Database Systems, Addison-Wesley, 1995.Google Scholar
  12. 12.
    M. Elmqvist, E. Jungert et al., “Terrain Modelling and Analysis using Laser Scanner Data”, Proceedings of Conference on Land Surface Mapping and Characterization using Laser Altmetry, Annapolis, MD, USA, October 22–24, 2001, pp 219–226, published by Dept. of Geography, University of Maryland, MD, 2001.Google Scholar
  13. 13.
    G. Grafe, “Query Evaluation Techniques for Large Databases”, ACM Computing Surveys, Vol. 25, No. 2, June 1993.Google Scholar
  14. 14.
    M. Jarke and J. Cohen, “Query Optimization in Database Systems”, ACM Computing Surveys, Vol. 16, No. 2, 1984.Google Scholar
  15. 15.
    F. V. Jensen, An Introduction to Bayesian Networks, Springer Verlag, New York, 1996.Google Scholar
  16. 16.
    E. Jungert, “An Information fusion System for Object Classification and Decision Support Using Multiple Heterogeneous Data Sources”, Proceedings of the 2nd International Conference on Information Fusion (Fusion’99), Sunnyvale, California, USA, July 6–8, 1999.Google Scholar
  17. 17.
    E. Jungert, U. Söderman, S. Ahlberg, P. Hörling, F. Lantz, G. Neider, “Generation of high resolution terrain elevation models for synthetic environments using laser-radar data”, Proceedings of SPIE no 3694, Modeling, Simulation and Visualization for Real And Virtual Environments, Orlando, Florida, April 7–8, 1999, pp 12–20.Google Scholar
  18. 18.
    E. Jungert, “A Qualitative Approach to Reasoning about Objects in Motion Based on Symbolic Projection”, Proceedings of the Conference on Multimedia Databases and Image Communication (MDIC’99), Salerno, Italy, October 4–5, 1999.Google Scholar
  19. 19.
    E. Jungert, “A Data Fusion Concept for a Query Language for Multiple Data Sources”, Proceedings of the 3rd International Conference on Information Fusion (FUSION 2000), Paris, France, July 10–13, 2000.Google Scholar
  20. 20.
    L. A. Klein, “A Boolean Algebra Approach to Multiple Sensor Voting Fusion”, IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 2, April 1993, pp 317–327.CrossRefGoogle Scholar
  21. 21.
    H. Kosch, M. Doller and L. Boszormenyi, “Content-based Indexing and Retrieval supported by Mobile Agent Technology”, Multimedia Databases and Image Communication, LNCS2184, (M. Tucci, ed.), Springer-Verlag, Berlin, 2001, pp 152–166.CrossRefGoogle Scholar
  22. 22.
    D. B. Lange and M. Oshima, Programming and Deploying Java Mobile Agents with Aglets, Addison-Wesley, Reading, MA, USA, 1999.Google Scholar
  23. 23.
    Lawrence Livermore National Laboratory, “Multisensor data fusion system using fuzzy logic”, in the web site on sensor technology at, 2001.
  24. 24.
    S.Y. Lee and F. J. Hsu, “Spatial Reasoning and Similarity Retrieval of images using 2D Cstring knowledge Representation”, Pattern Recognition, vol. 25, no 3, 1992, pp 305–318.CrossRefMathSciNetGoogle Scholar
  25. 25.
    J. R. Parker, “Multiple Sensors, Voting Methods and Target Value Analysis”, Proceedings of SPIE Conference on Signal Processing, Sensor Fusion and Target Recognition VI, SPIE vol. 3720, Orlando, Florida, April 1999, pp 330–335.Google Scholar
  26. 26.
    M. Stonebraker, “Implementation of Integrity Constraints and Views by Query Modification”, in SIGMOD, 1975.Google Scholar
  27. 27.
    J. D. Ullman, Database and Knowledge-base Systems, Vol. 1, Computer science Press, Rockville, Maryland, USA, 1988, pp 11–12.Google Scholar
  28. 28.
    Bienvenido Vélez, Ron Weiss, Mark A. Sheldon, and David K. Gifford, “Fast and Effective Query Refinement”, Proceedings of the 20th ACM Conference on Research and Development in Information Retrieval (SIGIR97), Philadelphia, Pennsylvania, July 1997.Google Scholar
  29. 29.
    E. Waltz and J. Llinas, Multisensor data fusion, Artect House, Boston, 1990.Google Scholar
  30. 30.
    F. E. White, “Managing Data Fusion Systems in Joint and Coalition Warfare”, Proceedings of EuroFusion98 — International Conference on Data Fusion, October 1998, Great Malvern, United Kingdom, pp 49–52.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Shi-Kuo Chang 
    • 1
  • Gennaro Costagliola
    • 2
  • Erland Jungert
    • 3
  1. 1.Department of Computer ScienceUniversity of PittsburghUSA
  2. 2.Dipartimento di Matematica ed InformaticaUniversità di SalernoUSA
  3. 3.Swedish Defense Research Agency (FOI)USA

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