Abstract
An efficient object perception is a crucial component of a mobile service robot. In this work we present a solution for visual categorization of objects. We developed a prototypic categorization system which classifies unknown objects based on their visual properties to a corresponding category of predefined domestic object categories. The system uses the Bag of Features approach which does not rely on global geometric object information. A major contribution of our work is the enhancement of the categorization accuracy and robustness through a selected combination of a set of supervised machine learners which are trained with visual information from object instances. Experimental results are provided which benchmark the behavior and verify the performance regarding the accuracy and robustness of the proposed system. The system is integrated on a mobile service robot to enhance its perceptual capabilities, hence computational cost and robot dependent properties are considered as essential design criteria.
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Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints (2004)
Duin, R.P.W.: The combining classifier: To train or not to train? In: International Conference on Pattern Recognition, vol. 2 (2002)
Dunn, J.C.: A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Cybernetics and Systems 3, 32–57 (1973)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Jiang, Y.G., Ngo, C.W., Yang, J.: Towards optimal bag-of-features for object categorization and semantic video retrieval (2007)
Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: ICCV 2005: Tenth IEEE Intl. Conf. on Computer Vision, vol. 1, pp. 604–610 (2005)
Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: Advances in Neural Information Processing Systems 19, pp. 985–992 (2006)
Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)
Wisspeintner, T., van der Zant, T., Iocchi, L., Schiffer, S.: Robocup@home 2008: Analysis of results. Tech. rep. (2008)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37(1), 1–19 (2004)
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Mueller, C.A., Hochgeschwender, N., Ploeger, P.G. (2012). Towards Robust Object Categorization for Mobile Robots with Combination of Classifiers. In: Röfer, T., Mayer, N.M., Savage, J., Saranlı, U. (eds) RoboCup 2011: Robot Soccer World Cup XV. RoboCup 2011. Lecture Notes in Computer Science(), vol 7416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32060-6_12
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DOI: https://doi.org/10.1007/978-3-642-32060-6_12
Publisher Name: Springer, Berlin, Heidelberg
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