Abstract
We describe the application of 1-Dimensional Kohonen Networks in the classification of color 2D images which has been evaluated in Popocatépetl Volcano’s images. The Popocatépetl, located in the limits of the State of Puebla in México, is active and under monitoring since 1997. We will consider one of the problems related with the question if our application of the Kohonen Network classifies according to the total intensity color of an image or well, if it classifies according to the connectivity, i.e. the topology, between the pixels that compose an image. In order to give arguments that support our hypothesis that our procedures share the classification according to the topology of the pixels in the images, we will present two approaches based a) in the evaluation of the classification given by the network when the pixels in the images are permuted; and,b) when an additional metric to the Euclidean distance is introduced.
Keywords
- Network Topology
- Weight Vector
- Training Image
- Image Classification
- Output Neuron
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© 2005 Springer-Verlag Berlin Heidelberg
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Pérez-Aguila, R., Gómez-Gil, P., Aguilera, A. (2005). Non-supervised Classification of 2D Color Images Using Kohonen Networks and a Novel Metric. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_29
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DOI: https://doi.org/10.1007/11578079_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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