Neural Computing and Applications

, Volume 30, Issue 3, pp 871–889 | Cite as

Human mimic color perception for segmentation of color images using a three-layered self-organizing map previously trained to classify color chromaticity

  • Farid García-LamontEmail author
  • Jair Cervantes
  • Asdrúbal López-Chau
Original Article


Most of the works addressing segmentation of color images use clustering-based methods; the drawback with such methods is that they require a priori knowledge of the amount of clusters, so the number of clusters is set depending on the nature of the scene so as not to lose color features of the scene. Other works that employ different unsupervised learning-based methods use the colors of the given image, but the classifying method employed is retrained again when a new image is given. Humans have the nature capability to: (1) recognize colors by using their previous knowledge, that is, they do not need to learn to identify colors every time they observe a new image and, (2) within a scene, humans can recognize regions or objects by their chromaticity features. Hence, in this paper we propose to emulate the human color perception for color image segmentation. We train a three-layered self-organizing map with chromaticity samples so that the neural network is able to segment color images by their chromaticity features. When training is finished, we use the same neural network to process several images, without training it again and without specifying, to some extent, the number of colors the image have. The hue component of colors is extracted by mapping the input image from the RGB space to the HSV space. We test our proposal using the Berkeley segmentation database and compare quantitatively our results with related works; according to the results comparison, we claim that our approach is competitive.


Self-organizing maps Color classification Image segmentation Color spaces 



This work was sponsored by Secretaría de Educación Pública: convenio PROMEP/103.5/13/6535. We thank Francisco Gallegos Funes for his valuable help and support.


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Farid García-Lamont
    • 1
    Email author
  • Jair Cervantes
    • 1
  • Asdrúbal López-Chau
    • 2
  1. 1.Universidad Autónoma del Estado de México, Centro Universitario UAEM TexcocoTexcoco-Estado de MéxicoMexico
  2. 2.Universidad Autónoma del Estado de México, Centro Universitario UAEM ZumpangoZumpango-Estado de MéxicoMexico

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