Semi-supervised Concept Detection by Learning the Structure of Similarity Graphs

  • Symeon Papadopoulos
  • Christos Sagonas
  • Ioannis Kompatsiaris
  • Athena Vakali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7732)

Abstract

We present an approach for detecting concepts in images by a graph-based semi-supervised learning scheme. The proposed approach builds a similarity graph between both the labeled and unlabeled images of the collection and uses the Laplacian Eigemaps of the graph as features for training concept detectors. Therefore, it offers multiple options for fusing different image features. In addition, we present an incremental learning scheme that, given a set of new unlabeled images, efficiently performs the computation of the Laplacian Eigenmaps. We evaluate the performance of our approach both on synthetic datasets and on MIR Flickr, comparing it with high-performance state-of-the-art learning schemes with competitive and in some cases superior results.

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References

  1. 1.
    Wang, M., Hua, X.-S., Tang, J., Hong, R.: Beyond distance measurement: constructing neighborhood similarity for video annotation. TMM 11(3), 465–476 (2009)Google Scholar
  2. 2.
    Zhu, X.: Semi-supervised learning with graphs. PhD Thesis, Carnegie Mellon University (2005) 0-542-19059-1Google Scholar
  3. 3.
    Zhou, D., Bousquet, O., Navin Lal, T., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: Advances in NIPS, vol. 16, pp. 321–328. MIT Press (2004)Google Scholar
  4. 4.
    Tang, J., et al.: Inferring semantic concepts from community contributed images and noisy tags. ACM Multimedia, 223–232 (2009)Google Scholar
  5. 5.
    Chen, X., et al.: Efficient large scale image annotation by probabilistic collaborative multi-label propagation. ACM Multimedia, 35–44 (2010)Google Scholar
  6. 6.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Mining and Knowledge Discovery 23(3), 447–478 (2011)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Macskassy, S.A., Provost, F.: Classification in Networked Data: A Toolkit and a Univariate Case Study. Journal of Machine Learning Research 8, 935–983 (2007)Google Scholar
  8. 8.
    Mikhail, B., Partha, N.: Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computing 15(6), 1373–1396 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Jia, P., Yin, J., Huang, X., Hu, D.: Incremental Laplacian eigenmaps by preserving adjacent information between data points. PR Letters 30(16), 1457–1463 (2009)Google Scholar
  10. 10.
    Leyffer, S., Mahajan, A.: Nonlinear Constrained Optimization: Methods and Software. Preprint ANL/MCS-P1729-0310 (2010)Google Scholar
  11. 11.
    Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A Library for Large Linear Classification. Journal of ML Research 9, 1871–1874 (2008)MATHGoogle Scholar
  12. 12.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)CrossRefGoogle Scholar
  13. 13.
    Huiskes, M.J., Michael, S., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: Proceedings of ACM Intern. Conference on Multimedia Information Retrieval (2008)Google Scholar
  14. 14.
    Hare, J.S., Lewis, P.H.: Automatically annotating the MIR Flickr dataset. In: ACM ICMR, pp. 547–556 (2010)Google Scholar
  15. 15.
    Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi supervised learning for image classification. In: Proceedings of IEEE CVPR Conference, pp. 902–909 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Symeon Papadopoulos
    • 1
  • Christos Sagonas
    • 1
  • Ioannis Kompatsiaris
    • 1
  • Athena Vakali
    • 2
  1. 1.Information Technologies InstituteCERTHGreece
  2. 2.Informatics DepartmentAristotle University of ThessalonikiGreece

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