Texture Classification with Neural Networks

  • William RaveaneEmail author
  • María Angélica González Arrieta
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)


Texture classification poses a well known difficulty within computer vision systems. This paper reviews a method for image segmentation based on the classification of textures using artificial neural networks. The supervised machine learning system developed here is able to recognize and distinguish among multiple feature regions within one or more photographs, where areas of interest are characterized by the various patterns of color and shape they exhibit. The use of an enhancement filter to reduce sensitivity to illumination and orientation changes in images is explored, as well as various post-processing techniques to improve the classification results based on context grouping. Various applications of the system are examined, including the geographical segmentation of satellite images and a brief overview of the model’s performance when employed on a real time video stream.


Neural Network Neural Network Model Color Channel Aerial Photography Neural Network System 
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 International Publishing Switzerland 2013

Authors and Affiliations

  • William Raveane
    • 1
    Email author
  • María Angélica González Arrieta
    • 1
  1. 1.Universidad de SalamancaSalamancaEspaña

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