Abstract
Real-time decision making based on visual sensory information is a demanding task for mobile robots. Learning on high-dimensional, highly redundant image data imposes a real problem for most learning algorithms, especially those being based on neural networks. In this paper we investigate the utilization of evolutionary techniques in combination with supervised learning of feedforward nets to automatically construct and improve suitable, task-dependent preprocessing layers helping to reduce the complexity of the original learning problem. Given a number of basic, parameterized low-level computer vision algorithms, the proposed evolutionary algorithm automatically selects and appropriately sets up the parameters of exactly those operators best suited for the imposed supervised learning problem.
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Bala, J., DeJong, K., Huang, J., Vafaie, H., Wechsler, H.: Hybrid Learning Using Genetic Algorithms and Decision Tress for Pattern Classification. In: 14th Int. Joint Conf. on Artifical Intelligence (IJCAI), Canada, pp. 719–724 (1995)
Belpaeme, T.: Evolution of Visual Feature Detectors. In: Proceedings of the First European Workshop on Evolutionary Computation in Image analysis and Signal Processing (EvoIASP 1999, Göteborg, Sweden), University of Birmingham School of Computer Science technical report (1999)
Braun, H., Weisbrod, J.: Evolving feedforward neural networks. In: Proc. of the International Conference on Artificial Neural Nets and Genetic Algorithms (1993)
Braun, H., Ragg, T.: Evolutionary Optimization of Neural Networks for Reinforcement Learning Algorithms. In: ICML 1996, Workshop Proceedings on Evolutionary Computing and Machine Learning, Italy, pp. 38–45 (1996)
McCabe, G.P.: Principal Variables. Technometrics 26, 127–134 (1984)
Draper., B.: Learning Object Recognition Strategies. Ph.D. dissertation, Univ. of Massachusetts, Dept. of Computer Science. Tech. report 93–50 (1993)
Draper, B.: Learning Control Strategies for Object Recognition. In: Ikeuchi, Veloso (eds.) Visual Learning. Oxford University Press, Oxford (1996)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (1986)
Krzanowski, W.J.: Selection of Variables to Preserve Multivariate Data Structure, Using Principal Component Analysis. Applied Statistics- Journal of the Royal Statistical Society Series C 36, 22–33 (1987)
Lange, S.: Verfolgung von farblich markierten Objekten in 2 Dimensionen. B.Sc. thesis, Universität Osnabrück, Institut für Kognitionswissenschaft (2001)
Martin, C.M.: The Simulated Evolution of Robot Perception. Ph.D. dissertation, Carnegie Mellon University Pittsburgh (2001)
Priese, L., Rehrmann, V., Schian, R., Lakmann, R.: Traffic Sign Recognition Based on Color Image Evaluation. In: Proc. Intelligent Vehicles Symposium, pp. 95–100 (1993)
De Ridder, D., Kouropteva, O., Okun, O., Pietikäinen, M., Duin, R.P.W.: Supervised locally linear embedding. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 333–341. Springer, Heidelberg (2003)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the Rprop algorithm. In: Proceedings of the ICNN, San Francisco (1993)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Smith, S.M.: A new class of corner finder. In: Proc. 3rd British Machine Vision Conference, pp. 139–148 (1992)
Steels, L., Kaplan, F.: AIBO’s first words. The social learning of language and meaning. In: Gouzoules, H. (ed.) Evolution of Communication, vol. 4(1). John Benjamins Publishing Company, Amsterdam (2001)
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Lange, S., Riedmiller, M. (2005). Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds) RoboCup 2004: Robot Soccer World Cup VIII. RoboCup 2004. Lecture Notes in Computer Science(), vol 3276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32256-6_15
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DOI: https://doi.org/10.1007/978-3-540-32256-6_15
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