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Flexible Shape-Based Query Rewriting

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Flexible Query Answering Systems (FQAS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4027))

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Abstract

A visual query is based on pictorial representation of conceptual entities and operations. One of the most important features used in visual queries is the shape. Despite its intuitive writing, a shape-based visual query usually suffers of a complexity processing related to two major parameters: 1-the imprecise user request, 2-shapes may undergo several types of transformation. Several methods are provided in the literature to assist the user during query writing. On one hand, relevance feedback technique is widely used to rewrite the initial user query. On the other hand, shape transformations are considered by current shape-based retrieval approaches without any user intervention. In this paper, we present a new cooperative approach based on the shape neighborhood concept allowing the user to rewrite a shape-based visual query according to his preferences with high flexibility in terms of including (or excluding) only some shape transformations and of result sorting.

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References

  1. Boaz, J.: Super, Improving Object Recognition Accuracy and Speed through Non-Uniform Sampling. In: Proc. of the SPIE Conference on Intelligent Robots and Computer SPIE, vol. 5267, pp. 228–239 (2003)

    Google Scholar 

  2. Sundar, H., Silver, D., Gagvani, N., Dickinson, S.: Skeleton Based Shape Matching and Retrieval. Shape Modeling International, 130–139 (2003)

    Google Scholar 

  3. Long, H., Leow, W.K.: Perceptual consistency improves image retrieval performance. In: ACM SIGIR, pp. 434–435 (2001)

    Google Scholar 

  4. Shah, J.: Skeletons of 3D Shapes. In: 5th international conference on Scale Space and PDE methods in computer vision, pp. 339–350 (2005)

    Google Scholar 

  5. Latecki, L.J., Lakaemper, R., Wolter, D.: Optimal Partial Shape Similarity. Image and vision computing, 227–236 (2005)

    Google Scholar 

  6. Carlin, M.: Measuring the Performance of Shape Similarity Retrieval Methods. SINTEF Electronics and cybernetics 84(1), 44–61 (2001)

    MATH  Google Scholar 

  7. Gaasterland, T., Godfrey, P., Minker, J.: An overview of cooperative answering. Journal of Intelligent Information Systems, 123–157 (1992)

    Google Scholar 

  8. Gaasterland, T., Godfrey, P., Minker, J.: Relaxation as a platform of cooperative answering. Journal of Intelligent Information Systems, 293–321 (1992)

    Google Scholar 

  9. Liu, T.-L.: A Generalized Shape Axis Model for Planar Shapes. In: IPCR, pp. 3491–3495 (2000)

    Google Scholar 

  10. Liu, T., Geiger, D.: Approximate Tree Matching and Shape Similarity. In: IEEE 17th International Conference on Computer Vision, pp. 456–462 (1999)

    Google Scholar 

  11. Liu, T., Geiger, D., Kohn, R.: Representation and Self-Similarity of Shapes. In: Int’l. Conf. Computer Vision, Bombay, pp. 1129–1138 (1998)

    Google Scholar 

  12. Chalhoub, G., Saad, S., Chbeir, R., Yetongnon, K.: Adaptive data retrieval in multimedia DBMS. CSITeA_04 Cairo (2004)

    Google Scholar 

  13. Bengoetxea, E.: Inexact Graph Matching Using Estimation of Distribution Algorithms, Ph.D thesis Ecole Nationale Supérieure des Télécommunications (Paris) (2002)

    Google Scholar 

  14. Bengoetxea, E.: Graph Matching as a combinatorial Optimization Problem With Constraints, Ecole Nationale Supérieure des Télécommunications (Paris) (2002)

    Google Scholar 

  15. Dionisio, J., Cardenas, A.: MQuery: A Visual Query Language for Multimedia, Timeline and Simulation Data. Journal of Visual Languages and Computing, 377–401 (1996)

    Google Scholar 

  16. Google (2005), http://www.google.com (last visited, 11/09/2005)

  17. Aufaure, P.: A High-Level Interface Language for GIS. Journal of Visual Languages and Computing 6(2), 167–182 (1995)

    Article  Google Scholar 

  18. Meyer, B.: Beyond Icons: Towards New Metaphors for Visual Query Languages for Spatial Information Systems. In: Proceedings of the first International Workshop on Interfaces to Database Systems, pp. 113–135 (1992)

    Google Scholar 

  19. Aufaure, M., Bonhomme, C., Lbath, A.: LVIS: Un Langage Visuel d’Interrogation de Bases de Données Spatiales. In: BDA 1998, Tunisie, pp. 527–545 (1998)

    Google Scholar 

  20. Xiaogang, X., Wenyin, L., Xiangyu, J., Zhengxing, S.: Sketch-based user interface for creative tasks. In: Proc. 5th Asia Pacific Conference on Computer Human Interaction HI 2002, pp. 560–570 (2002)

    Google Scholar 

  21. Haigh, K., Foslien, W., Guralnik, V.: Visual Query Language: Finding patterns in and relationships among time series data. In: Seventh Workshop on Mining Scientific and Engineering Datasets (April 24, 2004)

    Google Scholar 

  22. Zhang, H., Chen, Z., Liu, W.: Relevance Feedback in Content-Based Image Search, www.research.microsoft.com

  23. MacArthur, S.D., Brodley, C.E., Shyu, C.: Relevance Feedback Decision Trees in Content-Based Image Retrieval. In: Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries, June 2000, pp. 68–72 (2000)

    Google Scholar 

  24. Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE transactions on circuits and video technology, 644–655 (1998)

    Google Scholar 

  25. Chang, S., Costagliola, G., Pacini, G., Tucci, M.: Visual Language System for User Interfaces. IEEE Software, 33–44 (March 1995)

    Google Scholar 

  26. Goldin, D., et al.: Normalization of Life Science Data for Shape-based Similarity Querying, BECAT/CSE Technical Report TR-04-1 (January 2004)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Chalhoub, G., Chbeir, R., Yetongnon, K. (2006). Flexible Shape-Based Query Rewriting. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_36

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  • DOI: https://doi.org/10.1007/11766254_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34638-8

  • Online ISBN: 978-3-540-34639-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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