Fast Query Point Movement Techniques with Relevance Feedback for Content-Based Image Retrieval

  • Danzhou Liu
  • Kien A. Hua
  • Khanh Vu
  • Ning Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.


Voronoi Diagram Target Point Target Image Relevance Feedback Query Point 
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.
    Browne, P., Smeaton, A.F.: Video Information Retrieval Using Objects andOstensive Relevance Feedback. In: Proceedings of the ACM Symposium on Applied Computing (SAC), pp. 1084–1090 (2004)Google Scholar
  2. 2.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  3. 3.
    Chakrabarti, K., Michael, O.-B., Mehrotra, S., Porkaew, K.: Evaluating refined queries in top-k retrieval systems. IEEE Transactions on Knowledge and Data Engineering 16(2), 256–270 (2004)CrossRefGoogle Scholar
  4. 4.
    Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing 9(1), 20–37 (2000)CrossRefGoogle Scholar
  5. 5.
    Flickner, M., Sawhney, H.S., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The QBIC system. IEEE Computer 28(9), 23–32 (1995)Google Scholar
  6. 6.
    Gevers, T., Smeulders, A.: Content-based image retrieval: An overview. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision, Prentice Hall, Englewood Cliffs (2004)Google Scholar
  7. 7.
    Hua, K.A., Yu, N., Liu, D.: Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval. In: Proceedings of the IEEE ICDE Conference (2006)Google Scholar
  8. 8.
    Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: Proceedings of the 24th VLDB Conference, pp. 218–227 (1998)Google Scholar
  9. 9.
    Kim, D.-H., Chung, C.-W.: Qcluster: relevance feedback using adaptive clustering for content-based image retrieval. In: Proceedings of the ACM SIGMOD Conference, pp. 599–610 (2003)Google Scholar
  10. 10.
    Liu, D., Hua, K.A., Vu, K., Yu, N.: Efficient Target Search with Relevance Feedback for Large CBIR Systems. In: Proceedings of the 21st Annual ACM Symposium on Applied Computing (2006)Google Scholar
  11. 11.
    Michael, O.-B., Mehrotra, S.: Relevance feedback techniques in the MARS image retrieval systems. Multimedia Systems (9), 535–547 (2004)Google Scholar
  12. 12.
    Nakazato, M., Manola, L., Huang, T.S.: ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement. Journal of Visual Languages and Computing 14(4), 363–386 (2003)CrossRefGoogle Scholar
  13. 13.
    Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, New York (1985)Google Scholar
  14. 14.
    Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  15. 15.
    Shen, H.T., Ooi, B.C., Zhou, X.: Towards effective indexing for very large video sequence database. In: Proceedings of the ACM SIGMOD Conference, pp. 730–741 (2005)Google Scholar
  16. 16.
    Smeulders, A.W.M., Worring, M., Santini, A.G.S., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  17. 17.
    Smith, J.R., Chang, S.-F.: Transform features for texture classification and discrimination in large image databases. In: Proceedings of the International Conference on Image Processing, pp. 407–411 (1994)Google Scholar
  18. 18.
    Smith, J.R., Chang, S.-F.: VisualSEEk: A fully automated content-based image query system. In: Proceedings of the 4th ACM Multimedia Conference, pp. 87–98 (1996)Google Scholar
  19. 19.
    Stricker, M.A., Orengo, M.: Similarity of color images. In: Proceedings of Storage and Retrieval for Image and Video Databases (SPIE), pp. 381–392 (1995)Google Scholar
  20. 20.
    Vu, K., Hua, K.A., Tavanapong, W.: Image retrieval based on regions of interest. IEEE Transactions on Knowledge and Data Engineering 15(4), 1045–1049 (2003)CrossRefGoogle Scholar
  21. 21.
    Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)CrossRefGoogle Scholar
  22. 22.
    Wu, L., Faloutsos, C., Sycara, K., Payne, T.R.: FALCON: feedback adaptive loop for content-based retrieval. In: Proceedings of the 26th VLDB Conference, pp. 297–306 (2000)Google Scholar
  23. 23.
    Zhou, X.S., Huang, T.S.: Edge-based structural features for content-based image retrieval. Pattern Recognition Letters 22(5), 457–468 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Danzhou Liu
    • 1
  • Kien A. Hua
    • 1
  • Khanh Vu
    • 1
  • Ning Yu
    • 1
  1. 1.School of Computer ScienceUniversity of Central FloridaOrlandoUSA

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