Abstract
Aiming at improving the reliability of a recognition system, this paper presents a novel SVM-based rejection measurement (SVMM) and voting based combination methods of multiple classifier system (MCS) for pattern rejection. Compared with the previous heuristic designed criteria, SVMM is more straight-forward and can make use of much more information from the training data. The voting based combination methods for rejection is a preliminary attempt to adopt MCS for rejection. Comparison of SVMM with other well-known rejection criteria proves that it achieves the highest performance. Two different methods (structural modification and dataset re-sampling) are used to build MCSs. The basic classifier is the convolution neural network (CNN) which has achieved promising performances in numerous applications. Rejection based on MCS is then evaluated on MNIST and CENPARMI digit databases. Specifically, different rejection criteria (FRM, FTRM and SVMM) are individually combined with MCS for pattern rejection. Experimental results indicate that these combinations improve the rejection performance consistently and MCS built by dataset re-sampling works better than that with structural modification in rejection.
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References
Lauer, F., Suen, C.Y., Bloch, G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognition 40(6), 1816–1824 (2007)
Zhang, P., Bui, T.D., Suen, C.Y.: A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition 40(12), 3415–3429 (2007)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practice for convolutional neural networks applied to visual document analysis. In: Int’l Conf. on Document Analysis and Recognition, pp. 958–962 (2003)
Niu, X.X., Suen, C.Y.: A novel hybrid CNN-SVM classifier for recognition handwritten digits. Pattern Recognition 45(4), 1318–1325 (2012)
Liu, C.L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: investigation of normalization and feature extractor techniques. Pattern Recognition 37(2), 265–279 (2004)
He, C.L., Lam, L., Suen, C.Y.: A novel rejection measurement in handwritten numeral recognition on linear discriminant analysis. In: Int’l Conf. on Document Analysis and Recognition, pp. 451–455 (2009)
He, C.L., Lam, L., Suen, C.Y.: Rejection measurement based on linear discriminant analysis for document recognition. Int’l J. of Document Analysis and Recognition 14(3), 263–272 (2011)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machine. Journal ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)
Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: Int’l Conf. on Document Analysis and Recognition, pp. 1135–1139 (2011)
Wu, C., Fan, W., He, Y., Sun, J., Naoi, S.: Cascaded heterogeneous convolution neural networks for handwritten digit recognition. In: Int’l Conf. on Pattern Recognition, pp. 657–660 (2012)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Int’l Conf. Machine Learning, pp. 148–156 (1996)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Zhang, P., Bui, T.D., Suen, C.Y.: A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition 40(12), 3415–3429 (2007)
He, C.L., Suen, C.Y.: A hybrid multiple classifier system of unconstrained handwritten numeral recognition. Pattern Recognition and Image Analysis 17(4), 608–611 (2007)
Suen, C.Y., Lam, L.: Multiple classifier combination methodologies for different output levels. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 52–66. Springer, Heidelberg (2000)
Lam, L., Suen, C.Y.: Optimal combinations of pattern classifiers. Pattern Recognition Letters 16(9), 945–954 (1995)
Suen, C.Y., et al.: Computer recognition of unconstrained handwritten numerals. Proc. IEEE 80(7), 1162–1180 (1992)
LeCun, Y.: The MNIST database of handwritten digits, http://yann.lecun.com/exdb/mnist
Yuan, A., Bai, G., Jiao, L., Liu, Y.: Offline handwritten English character recognition based on convolutional neural network. In: Int’l Workshop Document Analysis Systems, pp. 125–129 (2012)
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Wang, WN., Zhang, XY., Suen, C.Y. (2013). A Novel Pattern Rejection Criterion Based on Multiple Classifiers. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_29
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DOI: https://doi.org/10.1007/978-3-642-38067-9_29
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