Abstract
The automatic selection of invariant feature variables is very important in pattern recognition systems. Recently, neural models have begun to be employed for this task. Among other models the ASSOM stands out because of its simplicity and biological plausibility. However, the main drawback of the application of the ASSOM in image processing systems is that a priori information is necessary to choose a suitable network size and topology in advance. The main purpose of this article is to present the Adaptive-Subspace Growing Cell Structures (ASGCS) network, which corresponds to a further improvement of the ASSOM that overcomes its main drawbacks. The ASGCS network introduces some GCS (Growing Cell Structures) concepts into the ASSOM model. The ASGCS network is described and some examples of automatic Gabor-like feature filter generation are given.
Preview
Unable to display preview. Download preview PDF.
References
Daugman, J.G. (1980). Two-dimensional spectral analysis of cortical receptive field profiles. Vision Research, 20, 847–856.
Fritzke, B. (1994). Growing Cell Structures—A self-organizing network for unsupervised and supervised learning. Neural Networks, Vol. 7, No. 9, 1441–1460.
Kohonen, T. (1995a). Self-Organizing Maps. Springer-Verlag, Heidelberg.
Kohonen, T. (1995b). The Adaptive-Subspace SOM (ASSOM) and its use for the implementation of invariant feature detection. Proc. of the Int. Conf. on Artificial Neural Networks—ICANN 95, October 9–13, Paris, France.
Kohonen, T. (1996). Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map. Biol. Cybern., Vol. 75, No. 4, 281–291.
Ruiz-del-Solar, J., and Köppen, M. (1996). Automatic generation of Oriented Filters for Texture Segmentation. Proc. of the Int. Workshop on Neural Networks for Identification, Control, Robotics & Signal/Image Processing—NICROSP 96, August 21–23, Venice, Italy.
Ruiz-del-Solar, J., and Köppen, M. (1997). A Texture Segmentation Architecture based on automatically generated Oriented Filters. Journal of Microelectronic Systems Integration, Vol. 5, No. 1, 43–52.
Ruiz-del-Solar, J. (1998). TEXSOM: Texture segmentation using self-organizing maps. Neurocomputing 21, 7–18.
Sanger, T.D. (1989). Optimal unsupervised learning in a single-layer linear feed-forward neural network. Neural Networks, Vol. 2, No. 6, 459–473.
Sirosh, J. (1995). A Self-Organizing neural network model of the primary visual cortex, Ph.D. Thesis, The University of Texas at Austin, USA.
Van Sluyters, R.C., Atkinson, J., Banks, M.S., Held, R.M., Hoffmann, K.-P., and Shatz, C.J. (1990). The Development of Vision and Visual Perception. In L. Spillman and J. Werner (Eds.), Visual Perception: The Neurophysiological Foundations, Academic Press.
Wilson, H.R., Levi, D., Maffei, L., Rovamo, J., and DeValois, R. (1990). The Perception of form: Retina to Striate Cortex. In L. Spillman and J. Werner (Eds.), Visual Perception: The Neurophysiological Foundations, Academic Press.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruiz-del-Solar, J., Kottow, D. (1999). ASGCS: A new self-organizing network for automatic selection of feature variables. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100548
Download citation
DOI: https://doi.org/10.1007/BFb0100548
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66068-2
Online ISBN: 978-3-540-48772-2
eBook Packages: Springer Book Archive