Abstract
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.
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References
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)
Denoeux, T., Masson, M.: Evidential reasoning in large partially ordered sets. Annals of Operations Research (May 2011)
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)
Duin, R.P.W.: The combining classifier: To train or not to train? In: ICPR (2), pp. 765–770 (2002)
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)
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)
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)
Le Borgne, H., Honnorat, N.: Fast shared boosting for large-scale concept detection. Multimedia Tools and Applications, 1–14 (2010)
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)
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)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
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)
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)
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)
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Znaidia, A., Le Borgne, H., Hudelot, C. (2012). Belief Theory for Large-Scale Multi-label Image Classification. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_24
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DOI: https://doi.org/10.1007/978-3-642-29461-7_24
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