Abstract
In this work we propose a novel pairwise diversity measure, that recalls the Fisher linear discriminant, to construct a classifier ensemble for tracking a non-rigid object in a complex environment. A subset of constantly updated classifiers is selected exploiting their capability to distinguish the target from the background and, at the same time, promoting independent errors. This reduced ensemble is employed in the target search phase, speeding up the application of the system and maintaining the performance comparable to state of the art algorithms. Experiments have been conducted on a Pan-Tilt-Zoom camera video sequence to demonstrate the effectiveness of the proposed approach coping with pose variations of the target.
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References
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)
Bian, S., Wang, W.: On diversity and accuracy of homogeneous and heterogeneous ensembles. Int. J. Hybrid Intell. Syst. 4(2), 103–128 (2007)
Brown, G., Wyatt, J.L., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Information Fusion 6(1), 5–20 (2005)
Chandra, A., Chen, H., Yao, X.: Trade-off between diversity and accuracy in ensemble generation. In: Jin, Y. (ed.) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol. 16, pp. 429–464. Springer, Heidelberg (2006)
Golestani, A., Ahmadian, K., Amiri, A., JahedMotlagh, M.-R.: A novel adaptive-boost-based strategy for combining classifiers using diversity concept. In: ACIS-ICIS, pp. 128–134 (2007)
Hong, L., Page, S.E.: Diversity and optimality. Research in Economics 98-08-077e, Santa Fe Institute (August 1998)
Kapp, M.N., Sabourin, R., Maupin, P.: An empirical study on diversity measures and margin theory for ensembles of classifiers. In: 10th International Conference on Information Fusion, July 2007, pp. 1–8 (2007)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Kohavi, R., Wolpert, D.H.: Bias plus variance decomposition for zero-one loss functions. In: Saitta, L. (ed.) International Conference on Machine Learning, pp. 275–283. Morgan Kaufmann, San Francisco (1996)
Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems 7, 231–238 (1995)
Kuncheva, L.I.: That elusive diversity in classifier ensembles. In: Perales, F.J., Campilho, A.C., Pérez, N., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 1126–1138. Springer, Heidelberg (2003)
Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles. Machine Learning 51, 181–207 (2003)
Kuncheva, L.I., Rodriguez, J.J.: Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering 19(4), 500–508 (2007)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Oza, N.C.: Online bagging and boosting. In: 2005 IEEE International Conference on Systems, Man and Cybernetics, October 2005, vol. 3, pp. 2340–2345 (2005)
Poh, N., Bengio, S.: How do correlation and variance of base classifiers affect fusion in biometric authentication tasks? IEEE Trans. on Sig. Processing 53(11), 4384–4396 (2005)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine 6(3), 21–45 (2006) (Third Quarter)
Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Machine Learning 65(1), 247–271 (2006)
Wenyao, L., Zhaohui, W., Pan, G.: An entropy-based diversity measure for classifier combining and its application to face classifier ensemble thinning. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 118–124. Springer, Heidelberg (2004)
Windeatt, T.: Accuracy/diversity and ensemble mlp classifier design. IEEE Transactions on Neural Networks 17(5), 1194–1211 (2006)
Udny Yule, G.: On the association of attributes in statistics. Philosophical Transactions of the Royal Society of London 194, 257–319 (1900)
Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1-2), 239–263 (2002)
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Visentini, I., Kittler, J., Foresti, G.L. (2009). Diversity-Based Classifier Selection for Adaptive Object Tracking. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_44
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DOI: https://doi.org/10.1007/978-3-642-02326-2_44
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