IbPRIA 2011: Pattern Recognition and Image Analysis pp 726-733 | Cite as
Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion
Conference paper
Abstract
One common approach to construction of highly accurate classifiers for hadwritten digit recognition is fusion of several weaker classifiers into a compound one, which (when meeting some constraints) outperforms all the individual fused classifiers. This paper studies the possibility of fusing classifiers of different kinds (Self-Organizing Maps, Randomized Trees, and AdaBoost with MB-LBP weak hypotheses) constructed on training sets resampled to different resolutions. While it is common to select one resolution of the input samples as the “ideal one” and fuse classifiers constructed for it, this paper shows that the accuracy of classification can be improved by fusing information from several scales.
Keywords
Digit Recognition Classifier Fusion MultiresolutionPreview
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References
- 1.Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 2.Alexandre, L.A.: Gender recognition: a multiscale decision fusion approach. Pattern Recognition Letters (2010), doi:10.1016/j.patrec.2010.02.010Google Scholar
- 3.Appiah, K., Hunter, A., Meng, H., Yue, S., Hobden, M., Priestley, N., Hobden, P., Pettit, C.: A binary self-organizing map and its FPGA implementation. In: IEEE International Joint Conference on Neural Networks (2009)Google Scholar
- 4.Breiman, L., Schapire, E.: Random forests. In: Machine Learning, pp. 5–32 (2001)Google Scholar
- 5.Brummer, N.: Focal multi-class: Toolkit for evaluation, fusion and calibration of multi-class recognition scores. Manual to SW Package (June 2007)Google Scholar
- 6.Carter, M.P.: Boosting a simple weak learner for classifying handwritten digits. Tech. Rep. PCS-TR98-341, Dartmouth College (1998)Google Scholar
- 7.Chang, S.F., He, J., Jiang, Y.G., Khoury, E.E., Ngo, C.W., Yanagawa, A., Zavesky, E.: Columbia university/VIREO-CityU/IRIT TRECVID2008 high-level feature extraction and interactive video search. In: NIST TRECVID Workshop (2008)Google Scholar
- 8.Ebrahimpour, R., Hamedi, S.: Hand written digit recognition by multiple classifier fusion based on decision templates approach. In: The International Conference on Computer, Electrical, and Systems Science, and Engineering, pp. 245–250 (2009)Google Scholar
- 9.Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)CrossRefGoogle Scholar
- 10.Geman, D., Amit, Y., Wilder, K.: Joint induction of shape features and tree classifiers. IEEE Trans. PAMI 19 (1997)Google Scholar
- 11.Kant, S., Sharma, V., Dass, B.K.: On recognition of cipher bit stream from different sources using majority voting fusion rule. Sci. Analysis Group, 90–111 (2006)Google Scholar
- 12.Kittler, J.: A framework for classifier fusion: Is it still needed? In: Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition, pp. 45–56. Springer, London (2000)Google Scholar
- 13.Kohonen, T.: The self-organizing map. Neurocomputing 21, 1–6 (1998)CrossRefMATHGoogle Scholar
- 14.Kussul, E.M., Baidyk, T.: Improved method of handwritten digit recognition tested on mnist database. Image Vision Comput. 22(12), 971–981 (2004)CrossRefGoogle Scholar
- 15.Labusch, K., Barth, E., Martinetz, T.: Simple method for high-performance digit recognition based on sparse coding. IEEE T. Neural Networks 19, 1985–1989 (2008)CrossRefGoogle Scholar
- 16.Lam, L., Suen, C.: Application of majority voting to pattern recognition: An analysis of its behavior and performance. SMC 27(5), 553–568 (1997)Google Scholar
- 17.Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognition 40(6), 1816–1824 (2007)CrossRefMATHGoogle Scholar
- 18.LeCun, Y., Cortes, C.: The mnist database of handwritten digits (2007), http://yann.lecun.com/exdb/mnist/ (cit. December 21, 2009)
- 19.Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 20.Minka, T.P.: A comparison of numerical optimizers for logistic regression. Tech. rep. (2003), http://research.microsoft.com/~minka/papers/logreg/
- 21.Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
- 22.Ruta, D., Gabrys, B.: An overview of classifier fusion methods. Computing and Information Systems 7, 1–10 (2000)Google Scholar
- 23.Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 297–336 (1999)CrossRefMATHGoogle Scholar
- 24.Seewald, A.K.: Digits - a dataset for handwritten digit recognition. Tech. Rep. TR-2005-27, Austrian Research Institut for Artificial Intelligence (2005)Google Scholar
- 25.Vaquero, D.A., Barrera, J., Hirata Jr., R.: A maximum-likelihood approach for multiresolution W-operator design. In: SIBGRAPI, pp. 71–78 (2005)Google Scholar
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