A Novel Retrieval Refinement and Interaction Pattern by Exploring Result Correlations for Image Retrieval

  • Rongrong Ji
  • Hongxun Yao
  • Shaohui Liu
  • Jicheng Wang
  • Pengfei Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)

Abstract

Efficient retrieval of image database that contains multiple predefined categories (e.g. medical imaging databases, museum painting collections) poses significant challenges and commercial prospects. By exploring category correlations of retrieval results in such scenario, this paper presents a novel retrieval refinement and feedback framework. It provides users a novel perceptual-similar interaction pattern for topic-based image retrieval. Firstly, we adopts Pairwise-Coupling SVM (PWC-SVM) to classify retrieval results into predefined image categories, and reorganizes them into category based browsing topics. Secondly, in feedback interaction, category operation is supported to capture users’ retrieval purpose fast and efficiently, which differs from traditional relevance feedback patterns that need elaborate image labeling. Especially, an Asymmetry Bagging SVM (ABSVM) network is adopted to precisely capture users’ retrieval purpose. And user interactions are accumulated to reinforce our inspections of image database. As demonstrated in experiments, remarkable feedback simplifications are achieved comparing to traditional interaction patterns based on image labeling. And excellent feedback efficiency enhancements are gained comparing to traditional SVM-based feedback learning methods.

Keywords

image retrieval image classification relevance feedback support vector machine pairwise coupling bagging 

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References

  1. 1.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  2. 2.
    Veltkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey, Technical report UU-CS-2000-34, Department of Computing Science, Utrecht University 34 (October 2000)Google Scholar
  3. 3.
    Gong, Y., Zhang, H.J., Chua, T.C.: An image database system with content capturing and fast image indexing abilities. In: Proc. IEEE Int. Conference on Multimedia Computing and Systems, Boston, 14-19 May, pp. 121–130 (1994)Google Scholar
  4. 4.
    Su, Z., Zhang, H., Li, S.: Relevance Feedback in Content-Based Image Retrieval: Bayesian Framework, Feature Subspaces and Progressive Learning. IEEE Tran. on Image Processing 12(3), 8 (2003)Google Scholar
  5. 5.
    Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(7) (July 2006)Google Scholar
  6. 6.
    Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  7. 7.
    Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: Proceeding of ACM International Con. on Multimedia, pp. 107–118 (2001)Google Scholar
  8. 8.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Tran. on System Man Cybern 3, 610–621 (1973)CrossRefGoogle Scholar
  9. 9.
    Rahman, M.M., Bhattacharya, P., Desai, B.C.: A Framework for Medical Image Retrieval using Machine Learning & Statistical Similarity Matching Techniques with Relevance Feedback, IEEE Trans. on Information Tech. in Biomedicine (accepted for future publication)Google Scholar
  10. 10.
    Wu, T., Lin, C.J., Weng, R.C.: Probability Estimates for Multi-Class Classification by Pairwise Coupling. Int. Journal on Machine Learning Research 10(5), 975–1005 (2004)Google Scholar
  11. 11.
    Rui, Y., Huang, T.S., Mehrotra, S., Ortega, M.: Relevance Feedback: A Power Tool for Interactive Content-based Image Retrieval. IEEE Trans. Circuits and Systems for Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  12. 12.
    Chen, Y., Wang, J.Z., Krovertz, R.: CLUE: Cluster-Based Retrieval of Image by Unsupervised Learning. IEEE Trans. on Image Processing 14(8), 1187–1201 (2005)CrossRefGoogle Scholar
  13. 13.
    Lee, K.-M., Nike Street, W.: Cluster-Driven Refinement for Content-Based Digital Image Retrieval. IEEE Trans. on Multimedia 6(6), 817–927 (2004)CrossRefGoogle Scholar
  14. 14.
    Tao, D., Tang, X., Li, X., Rui, Y.: Direct Kernel Biased Discriminant Analysis: A New Content-based Image Retrieval Relevance Feedback Algorithm. IEEE Trans. on Multimedia 8(4), 716–727 (2006)CrossRefGoogle Scholar
  15. 15.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabin, R.: Image Indexing using Color Correlogram. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, January, pp. 762–768 (1997)Google Scholar
  16. 16.
    Cui, H., Heidorn, P.B., Zhang, H.: An Approach to Automatic Classification of Text for Information Retrieval. In: The 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, Portland, OregonGoogle Scholar
  17. 17.
    Karanikolas, N., Skourlas, C., Christopoulou, A., Alevizos, T.: Medical Text Classification based on Text Retrieval techniques. In: MEDINF 2003, Craiova, Romania, October 9 - 11 (2003)Google Scholar
  18. 18.
    Liu, X., Gong, Y., Xu, W., Zhu, S.: Document clustering with cluster refinement and model selection capabilities. In: Proceedings of ACM SIGIR, SESSION: Clustering, Tampere, Finland, pp. 191–198 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rongrong Ji
    • 1
  • Hongxun Yao
    • 1
  • Shaohui Liu
    • 1
  • Jicheng Wang
    • 1
  • Pengfei Xu
    • 1
  1. 1.VILAB, School of Computer ScienceHarbin Institute of TechnologyHarbinChina

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