Feature extraction on vineyard by Gustafson Kessel FCM and K-means

  1. Correa, C. 1
  2. Valero, C. 1
  3. Barreiro, P. 1
  4. Diago, M.P. 2
  5. Tardáguila, J. 23
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  2. 2 Instituto de Ciencias de la Vid y del Vino
    info

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    ROR https://ror.org/01rm2sw78

  3. 3 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

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Libro:
Proceedings of the Mediterranean Electrotechnical Conference - MELECON

ISBN: 9781467307826

Año de publicación: 2012

Páginas: 481-484

Tipo: Capítulo de Libro

DOI: 10.1109/MELCON.2012.6196477 SCOPUS: 2-s2.0-84861494080 WoS: WOS:000309215000103 GOOGLE SCHOLAR

Resumen

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color images segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a wide class of images. However, it is not adequate for noisy images and it takes longer runtimes, as compared to other method like K-means. For this reason, several methods have been proposed to improve these weaknesses. Methods like Fuzzy C-Means with Gustafson-Kessel algorithm (FCM-GK), which improve its performance against the noise, but increase significantly the runtime. In this paper we propose to use the centroids generated by GK-FCM algorithms as seeding for K-means algorithm in order to accelerate the runtime and improve the performance of K-means with random seeding. These segmentation techniques were applied to feature extraction on vineyard images. Segmented images were evaluated using several quality parameters such as the rate of correctly classified area and runtime. © 2012 IEEE.