Abstract
Combining the predictions of a set of classifiers has shown to be an effective way of creating composite classifiers that are more accurate than any of the component classifiers; we have performed a research work consisting of the design, development and experimental use of a multi-classifier system for image analysis and surface classification of the different segments that might appear on a given picture in order to help a Mobile Robot in its navigation task. The presented approach combines a number of component classifiers which are standard machine learning classification algorithms, using a second layer paradigm to obtain a better classification accuracy. Experimental results have been obtained using a datafile of cases that contains information about surfaces, extracted from images obtained by the robot. The classification problem consists of recognizing to which of the surfaces belongs a n × n size subimage. The accuracy obtained using the presented new approach statistically improves those obtained using standard machine learning methods.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6, 37–66 (1991)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–284 (1988)
Deng, Y., Manjunath, B.S., Kenney, C., Moore, M.S., Shin, H.: An efficient color representation for image retrieval. IEEE Transactions on Image Processing 10(1), 140–147 (2001)
Dev, A., Kröse, B.J.A., Groen, F.C.A.: Navigation of a mobile robot on the temporal development of the optic flow. In: Proceedings IROS 1997, pp. 558–563. IEEE, Los Alamitos (1997)
Gunes, V., Ménard, M., Loonis, P., Petit-Renaud, S.: Combination, cooperation and selection of classifiers: A state of the art. International Journal of Pattern Recognition 17, 1303–1324 (2003)
Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)
Horswill, I.: Specialization of Perceptual Processes. PhD thesis, Massachusetts Institute of Technology (1993)
Kohavi, R.: The power of decision tables. In: Lavrač, N., Wrobel, S. (eds.) ECML 1995. LNCS, vol. 912, pp. 174–189. Springer, Heidelberg (1995)
Kohavi, R.: Scaling up the accuracy of Naive-Bayes classifiers: a decision-tree hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 202–207. AAAI Press, Menlo Park (1996)
Kohavi, R., Sommerfield, D., Dougherty, J.: Data mining using \(\mathcal{MLC}\)++: A machine learning library in \(\mathcal{C}++\). International Journal on Artificial Intelligence Tools 6(4), 537–566 (1997)
Minsky, M.: Steps towards artificial intelligence. Proceedings of the IRE 49, 8–30 (1961)
Monasterio, I., Lazkano, E., Rañó, I., Sierra, B.: Learning to traverse doors using visual information. Mathematics and Computers in Simulation 60, 347–356 (2002)
Murthy, S.K., Kasif, S., Salzberg, S.: A system for induction of oblique decision trees. Journal of Artificial Intelligence Research 2, 1–32 (1994)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Rañó, I., Lazkano, E., Zarautz, I., Monasterio, I., Sierra, B.: Mobile robot navigation using color image segmentation. Systems Science 4(27), 97–108 (2001)
Roli, F., Kittler, J., Windeatt, T. (eds.): MCS 2004. LNCS, vol. 3077. Springer, Heidelberg (2004)
Russ, J.C.: The Image Processing Handbook, 2nd edn. CRC Press, Boca Raton (1995)
Sierra, B., Rañó, I., Lazkano, E., Gisasola, U.: Machine learning approaches for image analysis: Recognition of hand orders by a mobile robot. In: Proceedings of the Third International Conference on Enterprise Information Systems, vol. I, pp. 330–335 (2001)
Sierra, B., Serrano, N., Larrañaga, P., Plasencia, E.J., Inza, I., Jiménez, J.J., Revuelta, P., Mora, M.L.: Using bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artificial Intelligence in Medicine 22(3), 233–248 (2001)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945)
Wolpert, D.H.: Stacked generalization. Neural Networks 5, 241–259 (1992)
Xu, L., Kryzak, A., Suen, C.Y.: Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on SMC 22, 418–435 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Otzeta, J.M., Sierra, B., Lazkano, E. (2005). Image Analysis and Automatic Surface Identification by a Bi-level Multi-classifier. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds) Brain, Vision, and Artificial Intelligence. BVAI 2005. Lecture Notes in Computer Science, vol 3704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11565123_45
Download citation
DOI: https://doi.org/10.1007/11565123_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29282-1
Online ISBN: 978-3-540-32029-6
eBook Packages: Computer ScienceComputer Science (R0)