Non-supervised Classification of 2D Color Images Using Kohonen Networks and a Novel Metric

  • Ricardo Pérez-Aguila
  • Pilar Gómez-Gil
  • Antonio Aguilera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ricardo Pérez-Aguila
    • 1
  • Pilar Gómez-Gil
    • 1
  • Antonio Aguilera
    • 1
  1. 1.Departamento de Ingeniería en Sistemas Computacionales, Centro de Investigación en Tecnologías de Información y Automatización (CENTIA)Universidad de las Américas – Puebla (UDLAP), Ex-Hacienda Santa Catarina MártirCholulaMéxico

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