Published

2016-05-01

Segmentation of color images by chromaticity features using self-organizing maps

Segmentación de imágenes de color por características cromáticas empleando mapas auto-organizados

Keywords:

Segmentation of color images, color spaces, competitive neural networks (en)
Segmentación de imágenes de color, espacios de color, redes neuronales competitivas (es)

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Authors

  • Farid García-Lamont Universidad Autónoma del Estado de México. Centro Universitario UAEM Texcoco https://orcid.org/0000-0002-9739-3802
  • Alma Delia Cuevas Rasgado Universidad Autónoma del Estado de México. Centro Universitario UAEM Texcoco
  • Yedid Erandini Niño Membrillo Universidad Autónoma del Estado de México. Centro Universitario UAEM Texcoco

Usually, the segmentation of color images is performed using cluster-based methods and the RGB space to represent the colors. The drawback with these methods is the a priori knowledge of the number of groups, or colors, in the image; besides, the RGB space issensitive to the intensity of the colors. Humans can identify different sections within a scene by the chromaticity of its colors of, as this is the feature humans employ to tell them apart. In this paper, we propose to emulate the human perception of color by training a self-organizing map (SOM) with samples of chromaticity of different colors. The image to process is mapped to the HSV space because in this space the chromaticity is decoupled from the intensity, while in the RGB space this is not possible. Our proposal does not require knowing a priori the number of colors within a scene, and non-uniform illumination does not significantly affect the image segmentation. We present experimental results using some images from the Berkeley segmentation database by employing SOMs with different sizes, which are segmented successfully using only chromaticity features.

Usualmente, la segmentación de imágenes de color se realiza empleando métodos de agrupamiento y el espacio RGB para representar los colores. El problema con los métodos de agrupamiento es que se requiere conocer previamente la cantidad de grupos, o colores, en la imagen; además de que el espacio RGB es sensible a la intensidad de colores. Los humanos podemos identificar diferentes secciones de una escena solo por la cromaticidad de los colores, ya que representa la característica que nos permite diferenciarlos entre sí. En este artículo se propone emular la percepción humana del color al entrenar un mapa auto-organizado (MAO) con muestras de cromaticidad de diferentes colores. La imagen a procesar es transformada al espacio HSV porque en tal espacio la cromaticidad es separada de la intensidad, mientras que en el espacio RGB no es posible. Nuestra propuesta no requiere conocer previamente la cantidad de colores que hay en una escena, y la iluminación no uniforme no afecta significativamente la segmentación de la imagen. Presentamos resultados experimentales utilizando algunas imágenes de la base de segmentación de Berkeley empleando MAOs de diferentes tamaños, las cuales son segmentadas exitosamente empleando únicamente características de cromaticidad.

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Copyright (c) 2016 Farid García-Lamont, Alma Delia Cuevas Rasgado, Yedid Erandini Niño Membrillo

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