Abstract
In this paper, several one-class classification methods are investigated in pixel space and PCA (Principal component Analysis) subspace having in mind the need of finding suitable learning and classification methods to support natural language grounding in the context of Human-Robot Interaction. Face and non-face classification is used as an example to demonstrate effectiveness of these one-class classifiers. The idea is to train target class models with only target (face) patterns, but still keeping good discrimination over outlier (never seen non-target) patterns. Some discussion is given and promising results are reported.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
AT & T Face Database, formerly The ORL Database of Faces, at http://www.uk.research.att.com/ facedatabase.html
Bishop, C.: Novelty detection and neural network validation. In: IEEE Proc. Vision, Image and Signal Processing vol.141, pp. 217–222 (1994)
Bishop, C.: Neural Networks for Pattern recognition. Oxford University Press, Oxford (1995)
Duin, R.: PRTOOLS 4.0. Delft University of Technology, The Netherlands (2004)
Japkowicz, N.: Concept-Learning in the absence of counter-examples: an autoassociation based approach to classification. Ph D thesis, The State Univ. of New Jersy (1999)
Meng, L.M.: An Image-based Bayesian Framework for Face detection. In: Proc. of IEEE Intl. Conf. On Computer Vision and Pattern Recognition (2000)
Moya, M., Koch, M., Hostetler, L.: One-class classifier networks for target recognition applications. In: Proc. World congress on neural networks, pp. 797–801 (1993)
Muggleton, S., Firth, J.: CProgol4.4: a tutorial introduction. In: Dzeroski, S., Lavrac, N. (eds.) Relational Data Mining, pp. 160–188. Springer, Heidelberg (2001)
Parra, L., Deco, G., Miesbach, S.: Statistical independence and novelty detection with information preserving nonlinear maps. Neural Compiutation 8, 260–269 (1996)
Pekalska, E., Tax, D.M.J., Duin, R.P.W.: One-Class LP Classifiers for dissimilarity Representations. In: Advances in Neural Info. Processing Systems, vol. 15, pp. 761–768. MIT Press, Cambridge (2003)
Pham, T.V., Arnold, M.W., Smeulders, W.M.: Face Detection by aggregated Bayesian network classifiers. Pattern Recognition Letters 23(4), 451–461 (2002)
Ritter, G., Gallegos, M.: Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognition Letters 18, 525–539 (1997)
Roberts, S., Penny, W.: Novelty, confidence and errors in connectionist systems. Technology report, Imperial College, London, TR-96-1 (1996)
Schneiderman, H., Kanade, K.: A Statistical Method for 3D Object Detection Applied to Faces and Cars. In: Proc. CVPR 2000, pp. 746–751 (2000)
Seabra Lopes, L.: Carl: from Situated Activity to Language-Level Interaction and Learning. In: Proc. IEEE Intl. Conf. on Intelligent Robotics & Systems, pp. 890–896 (2002)
Seabra Lopes, L., Wang, Q.H.: Towards Grounded Human-Robot Communication. In: Proc. IEEE Intl. Workshop RO-MAN, pp. 312–318 (2002)
Tax, David M. J.: One-class classification. Ph D dissertation, Delft University of Technology, The Netherlands (2001)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1994)
Wang, Q.H., Seabra Lopes, L.: An Object Recognition Framework Based on Hidden Markov Trees and Kullback-Leibler Distance. In: Proc. ACCV 2004, pp. 276–281 (2004)
Yang, M.H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Trans. PAMI 24, 34–58 (2002)
Yang, M.H., Kriegman, D., Ahuja, N.: Face Detection Using Multimodal Density Models. Computer Vision and Image Understanding 84, 264–284 (2001)
Zhu, Y., Schwartz, S.: Efficient Face Detection with Multiscale Sequential Classification. In: Proc. IEEE Intl. Conf. Image Processing 2002, pp. 121–124 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Q., Lopes, L.S., Tax, D.M.J. (2004). Visual Object Recognition Through One-Class Learning. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_58
Download citation
DOI: https://doi.org/10.1007/978-3-540-30125-7_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
eBook Packages: Springer Book Archive