Belief Theory for Large-Scale Multi-label Image Classification

  • Amel Znaidia
  • Hervé Le Borgne
  • Céline Hudelot
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


Classifier combination is known to generally perform better than each individual classifier by taking into account the complementarity between the input pieces of information. Dempster-Shafer theory is a framework of interest to make such a fusion at the decision level, and allows in addition to handle the conflict that can exist between the classifiers as well as the uncertainty that remains on the sources of information. In this contribution, we present an approach for classifier fusion in the context of large-scale multi-label and multi-modal image classification that improves the classification accuracy. The complexity of calculations is reduced by considering only a subset of the frame of discernment. The classification results on a large dataset of 18,000 images and 99 classes show that the proposed method gives higher performances than of those classifiers separately considered, while keeping tractable computational cost.


Demspster-Shafer theory multi-label classification multi-modal classification classifier fusion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Binder, A., Samek, W., Kloft, M., Müller, C., Müller, K.-R., Kawanabe, M.: The joint submission of the tu berlin and fraunhofer first (tubfi) to the imageclef 2011 photo annotation task. In: CLEF (Notebook Papers/Labs/Workshop) (2011)Google Scholar
  2. 2.
    Denoeux, T., Masson, M.: Evidential reasoning in large partially ordered sets. Annals of Operations Research (May 2011)Google Scholar
  3. 3.
    Dubois, D., Prade, H., Smets, P.: New Semantics for Quantitative Possibility Theory. In: Benferhat, S., Besnard, P. (eds.) ECSQARU 2001. LNCS (LNAI), vol. 2143, pp. 410–421. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Duin, R.P.W.: The combining classifier: To train or not to train? In: ICPR (2), pp. 765–770 (2002)Google Scholar
  5. 5.
    Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 902–909 (June 2010)Google Scholar
  6. 6.
    Huiskes, M.J., Lew, M.S.: The mir flickr retrieval evaluation. In: MIR 2008: Proceedings of the 2008 ACM International Conference on Multimedia Information Retrieval. ACM, New York (2008)Google Scholar
  7. 7.
    Kawanabe, M., Binder, A., Muller, C., Wojcikiewicz, W.: Multi-modal visual concept classification of images via markov random walk over tags. In: Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision, WACV (2011)Google Scholar
  8. 8.
    Le Borgne, H., Honnorat, N.: Fast shared boosting for large-scale concept detection. Multimedia Tools and Applications, 1–14 (2010)Google Scholar
  9. 9.
    Liu, N., Dellandréa, E., Tellez, B., Chen, L.: Associating textual features with visual ones to improve affective image classification. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 195–204. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Quost, B., Masson, M.-H., Denoeux, T.: Classifier fusion in the dempster–shafer framework using optimized t-norm based combination rules. Int. J. Approx. Reasoning 52, 353–374 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  12. 12.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1349–1380 (2000)CrossRefGoogle Scholar
  13. 13.
    Tax, D.M., van Breukelen, M., Duin, R.P., Kittler, J.: Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33(9), 1475–1485 (2000)CrossRefGoogle Scholar
  14. 14.
    Znaidia, A., Borgne, H.L., Popescu, A.: Cea list’s participation to visual concept detection task of imageclef 2011. In: CLEF (Notebook Papers/Labs/Workshop) (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Amel Znaidia
    • 1
  • Hervé Le Borgne
    • 1
  • Céline Hudelot
    • 2
  1. 1.Laboratory of Vision and Content EngineeringCEA, LISTGif-sur-YvetteFrance
  2. 2.MAS LaboratoryEcole Centrale de ParisChatenay-Malabry CedexFrance

Personalised recommendations