A Self-immunizing Manifold Ranking for Image Retrieval

  • Jun Wu
  • Yidong Li
  • Songhe Feng
  • Hong Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)


Manifold ranking (MR), as a powerful semi-supervised learning algorithm, plays an important role to deal with the relevance feedback problem in content-based image retrieval (CBIR). However, conventional MR has two main drawbacks: 1) in many cases, it is prone to exploit “unreliable” unlabeled images when deployed in CBIR due to the semantic gap; 2) the performance of MR is quite sensitive to the scale parameter used for calculating the Laplacian matrix. In this work, a self-immunizing MR approach is presented to address the drawbacks. Concretely, we first propose an elastic kNN graph as well as its constructing algorithm to exploit unlabeled images “safely”, and then develop a local scaling solution to calculate the Laplacian matrix adaptively. Extensive experiments on 10,000 Corel images show that the proposed algorithm is more effective than the state-of-the-art approaches.


content-based image retrieval relevance feedback self-immunizing manifold ranking elastic kNN graph local scaling 


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  1. 1.
    Chapelle, O., Scholkope, B., Zien, A.: Semisupervised Learning. MIT Press, Cambridge (2006)Google Scholar
  2. 2.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of The New Age. ACM Comput. Surv. 40(2), 5:1-5:60 (2008)Google Scholar
  3. 3.
    He, J., Li, M., Zhang, H., Tong, H., Zhang, C.: Manifold-Ranking Based Image Retrieval. In: Proc. ACM Int. Conf. Multimedia, MM (2004)Google Scholar
  4. 4.
    Hoi, S.C.H., Jin, R., Zhu, J., Lyu, M.R.: Semi-Supervised SVM Batch Mode Active Learning for Image Retrieval. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  5. 5.
    Li, Y.F., Zhou, Z.H.: Towards Making Unlabeled Data Never Hurt. In: Proc. Int. Conf. Machine Learning, ICML (2011)Google Scholar
  6. 6.
    Luxberg, U.: A Tutorial on Spectral Clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, B., Pan, F., Hu, K.M., Paul, J.C.: Manifold-Ranking Based Retrieval using k-Regular Nearest Neighbor Graph. Pattern Recognition 45(4), 1569–1577 (2012)CrossRefGoogle Scholar
  8. 8.
    Wu, J., Lin, Z., Lu, M.: Asymmetric Semi-Supervised Boosting for SVM Active Learning in CBIR. In: Proc. ACM Int. Conf. Image and Video Retrieval, CIVR (2010)Google Scholar
  9. 9.
    Wu, J., Lu, M., Wang, C.: Collaborative Learning between Visual Content and Hidden Semantic for Image Retrieval. In: Proc. IEEE Int. Conf. Data Mining, ICDM (2010)Google Scholar
  10. 10.
    Xu, B., Bu, J., Chen, C., Cai, D., He, X., Liu, W., Luo, J.: Efficient Manifold Ranking for Image Retrieval. In: Proc. ACM Int. Conf. Research and Development in Information Retrieval, SIGIR (2011)Google Scholar
  11. 11.
    Yang, Y., Nie, F., Xu, D., Luo, J., Zhuang, Y., Pan, Y.: A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback. IEEE Trans. Pattern Analysis and Machine Intelligence 34(4), 723–742 (2012)CrossRefGoogle Scholar
  12. 12.
    Zelnik-Manor, L., Perona, P.: Self-Tuning Spectral Clustering. Adv. Neural. Inf. Process. Syst. (NIPS) 2 (2004)Google Scholar
  13. 13.
    Zhang, L., Wang, L., Lin, W.: Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval. IEEE Trans. Image Processing 21(4), 2294–2308 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhou, X.S., Huang, T.S.: Relevance Feedback in Image Retrieval: A Comprehensive Review. Multimedia Syst. 8(6), 536–544 (2003)CrossRefGoogle Scholar
  15. 15.
    Zhou, Z.H., Chen, K.J., Dai, H.B.: Enhancing Relevance Feedback in Image Retrieval using Unlabeled Data. ACM Transactions on Information Systems 24(2), 219–244 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jun Wu
    • 1
  • Yidong Li
    • 1
  • Songhe Feng
    • 1
  • Hong Shen
    • 1
    • 2
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.School of Computer ScienceUniversity of AdelaideAustralia

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