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Automatic computing of number of clusters for color image segmentation employing fuzzy c-means by extracting chromaticity features of colors

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Abstract

In this paper we introduce a method for color image segmentation by computing automatically the number of clusters the data, pixels, are divided into using fuzzy c-means. In several works the number of clusters is defined by the user. In other ones the number of clusters is computed by obtaining the number of dominant colors, which is determined with unsupervised neural networks (NN) trained with the image’s colors; the number of dominant colors is defined by the number of the most activated neurons. The drawbacks with this approach are as follows: (1) The NN must be trained every time a new image is given and (2) despite employing different color spaces, the intensity data of colors are used, so the undesired effects of non-uniform illumination may affect computing the number of dominant colors. Our proposal consists in processing the images with an unsupervised NN trained previously with chromaticity samples of different colors; the number of the neurons with the highest activation occurrences defines the number of clusters the image is segmented. By training the NN with chromatic data of colors it can be employed to process any image without training it again, and our approach is, to some extent, robust to non-uniform illumination. We perform experiments with the images of the Berkeley segmentation database, using competitive NN and self-organizing maps; we compute and compare the quantitative evaluation of the segmented images obtained with related works using the probabilistic random index and variation of information metrics.

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Notes

  1. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/.

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Authors and Affiliations

  1. Centro Universitario UAEM Texcoco, Universidad Autónoma del Estado de México, Av. Jardín Zumpango s/n, Fraccionamiento El Tejocote, CP 56259, Texcoco-Estado de México, Mexico

    Farid García-Lamont & Jair Cervantes

  2. Centro Universitario UAEM Zumpango, Universidad Autónoma del Estado de México, Camino viejo a Jilotzingo continuación Calle Rayón, CP 55600, Zumpango-Estado de México, Mexico

    Asdrúbal López-Chau

  3. Facultad de Informática, Universidad Autónoma de Sinaloa, Josefa Ortiz de Domínguez s/n, Cd. Universitaria, CP 80013, Culiacán-Sinaloa, Mexico

    Arturo Yee-Rendón

Authors
  1. Farid García-Lamont
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  2. Jair Cervantes
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  3. Asdrúbal López-Chau
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  4. Arturo Yee-Rendón
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Correspondence to Farid García-Lamont.

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García-Lamont, F., Cervantes, J., López-Chau, A. et al. Automatic computing of number of clusters for color image segmentation employing fuzzy c-means by extracting chromaticity features of colors. Pattern Anal Applic 23, 59–84 (2020). https://doi.org/10.1007/s10044-018-0729-9

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  • DOI: https://doi.org/10.1007/s10044-018-0729-9

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