Skip to main content

A Learning State-Space Model for Image Retrieval


This paper proposes an approach based on a state-space model for learning the user concepts in image retrieval. We first design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. The design of the concept units aims at describing similar characteristics at a certain perspective among relevant images. We present the details of our proposed approach based on a state-space model for interactive image retrieval, including likelihood and transition models, and we also describe some experiments that show the efficacy of our proposed model. This work demonstrates the feasibility of using a state-space model to estimate the user intuition in image retrieval.


  1. 1.

    Datta R, Li J, Wang JZ: Content-based image retrieval: approaches and trends of the new age. Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR '05), November 2005, Singapore 253–262.

    Google Scholar 

  2. 2.

    Lew MS, Sebe N, Djeraba C, Jain R: Content-based multimedia information retrieval: state of the art and challenges. ACM Transactions on Multimedia Computing, Communications and Applications 2006,2(1):1-19. 10.1145/1126004.1126005

    Article  Google Scholar 

  3. 3.

    Goh K, Li B, Chang EY: Semantics and feature discovery via confidence-based ensemble. ACM Transactions on Multimedia Computing, Communications, and Applications 2005,1(2):168-189. 10.1145/1062253.1062257

    Article  Google Scholar 

  4. 4.

    Rui Y, Huang TS, Ortega M, Mehrotra S: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 1998,8(5):644-655. 10.1109/76.718510

    Article  Google Scholar 

  5. 5.

    Zhou XS, Huang TS: Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems 2003,8(6):536-544. 10.1007/s00530-002-0070-3

    Article  Google Scholar 

  6. 6.

    Cox IJ, Miller ML, Minka TP, Papathomas TV, Yianilos PN: The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing 2000,9(1):20-37. 10.1109/83.817596

    Article  Google Scholar 

  7. 7.

    Su Z, Zhang H, Li S, Ma S: Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Transactions on Image Processing 2003,12(8):924-937. 10.1109/TIP.2003.815254

    Article  Google Scholar 

  8. 8.

    Vasconcelos N, Lippman A: Learning from user feedback in image retrieval systems. Proceedings of Advances in Neural Information Processing Systems (NIPS '99), November-December 1999, Denver, Colo, USA 977–986.

    Google Scholar 

  9. 9.

    Jing F, Li M, Zhang H-J, Zhang B: An efficient and effective region-based image retrieval framework. IEEE Transactions on Image Processing 2004,13(5):699-709. 10.1109/TIP.2004.826125

    Article  Google Scholar 

  10. 10.

    Goh K-S, Chang EY, Lai W-C: Multimodal concept-dependent active learning for image retrieval. Proceedings of the 12th Annual ACM International Conference on Multimedia, October 2004, New York, NY, USA 564–571.

    Google Scholar 

  11. 11.

    Carson C, Thomas M, Belongie S, Hellerstein JM, Malik J: Blobworld: a system for region-based image indexing and retrieval. Proceedings of the 3rd International Conference on Visual Information and Information Systems (VISUAL '99), June 1999, Amsterdam, The Netherlands 509–516.

    Google Scholar 

  12. 12.

    Wang JZ, Li J, Wiederhold G: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001,23(9):947-963. 10.1109/34.955109

    Article  Google Scholar 

  13. 13.

    Barnard K, Forsyth D: Learning the semantics of words and pictures. Proceedings of the 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 2: 408–415.

    Google Scholar 

  14. 14.

    Fei-Fei L, Perona P: A Bayesian hierarchical model for learning natural scene categories. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 2005, San Diego, Calif, USA 2: 524–531.

    Google Scholar 

  15. 15.

    Feng SL, Manmatha R, Lavrenko V: Multiple Bernoulli relevance models for image and video annotation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 2: 1002–1009.

    Google Scholar 

  16. 16.

    Jeon J, Lavrenko V, Manmatha R: Automatic image annotation and retrieval using cross-media relevance models. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '03), July-August 2003, Toronto, Ont, Canada 119–126.

    Google Scholar 

  17. 17.

    Heisterkamp DR: Building a latent semantic index of an image database from patterns of relevance feedback. Proceedings of the 16th International Conference on Pattern Recognition (ICPR '02), August 2002, Quebec, Canada 4: 134–137.

    Article  Google Scholar 

  18. 18.

    Shah-Hosseini A, Knapp GM: Learning image semantics from users relevance feedback. Proceedings of the 12th Annual ACM International Conference on Multimedia, October 2004, New York, NY, USA 452–455.

    Google Scholar 

  19. 19.

    Arulampalam MS, Maskell S, Gordon N, Clapp T: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002,50(2):174-188. 10.1109/78.978374

    Article  Google Scholar 

  20. 20.

    Ghahramani Z: An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence 2001,15(1):9-42. 10.1142/S0218001401000836

    Article  Google Scholar 

  21. 21.

    Murphy KP: Dynamic Bayesian networks: representation, inference and learning, Ph.D. thesis. University of California, Berkeley, Calif, USA; 2002.

    Google Scholar 

  22. 22.

    Duda RO, Hart PE, Stork DG: Pattern Classification. 2nd edition. John Wiley & Sons, New York, NY, USA; 2001.

    MATH  Google Scholar 

  23. 23.

    Fergus R, Fei-Fei L, Perona P, Zisserman A: Learning object categories from Google's image search. Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), October 2005, Beijing, China 2: 1816–1823.

    Article  Google Scholar 

  24. 24.

    Wang D: A multiscale gradient algorithm for image segmentation using watersheds. Pattern Recognition 1997,30(12):2043-2052. 10.1016/S0031-3203(97)00015-0

    Article  Google Scholar 

  25. 25.

    Mezaris V, Kompatsiaris I, Strintzis MG: An ontology approach to object-based image retrieval. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 2: 511–514.

    Google Scholar 

  26. 26.

    Srikanth M, Varner J, Bowden M, Moldovan DI: Exploiting ontologies for automatic image annotation. Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '05), August 2005, Salvador, Brazil 552–558.

    Google Scholar 

  27. 27.

    Atmosukarto I, Leow WK, Huang Z: Feature combination and relevance feedback for 3D model retrieval. Proceedings of the 11th International Multimedia Modelling Conference (MMM '05), January 2005, Melbourne, Australia 334–339.

    Google Scholar 

  28. 28.

    Fei-Fei L, Fergus R, Perona P: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Proceedings of IEEE CVPR Workshop of Generative Model Based Vision (WGMBV '04), June 2004, Washington, DC, USA

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Cheng-Chieh Chiang.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Chiang, CC., Hung, YP. & Lee, G.C. A Learning State-Space Model for Image Retrieval. EURASIP J. Adv. Signal Process. 2007, 083526 (2007).

Download citation


  • Information Technology
  • Feature Space
  • Region Scale
  • Quantum Information
  • Image Segmentation