Image Segmentation Using Ant System-based Clustering Algorithm

Jevtić, Aleksandar and Quintanilla Domínguez, Joel and Barron Adame, Jose Miguel and Andina de la Fuente, Diego (2011). Image Segmentation Using Ant System-based Clustering Algorithm. In: "6th International Conference SOCO 2011", 06/04/2011 - 08/04/2011, Salamanca, España. ISBN 978-3-642-19643-0. pp. 35-45. https://doi.org/10.1007/978-3-642-19644-7_62.

Description

Title: Image Segmentation Using Ant System-based Clustering Algorithm
Author/s:
  • Jevtić, Aleksandar
  • Quintanilla Domínguez, Joel
  • Barron Adame, Jose Miguel
  • Andina de la Fuente, Diego
Item Type: Presentation at Congress or Conference (Article)
Event Title: 6th International Conference SOCO 2011
Event Dates: 06/04/2011 - 08/04/2011
Event Location: Salamanca, España
Title of Book: Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011
Date: 2011
ISBN: 978-3-642-19643-0
Volume: 87
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.

More information

Item ID: 13260
DC Identifier: http://oa.upm.es/13260/
OAI Identifier: oai:oa.upm.es:13260
DOI: 10.1007/978-3-642-19644-7_62
Official URL: http://link.springer.com/chapter/10.1007/978-3-642-19644-7_5
Deposited by: Memoria Investigacion
Deposited on: 28 Nov 2012 11:33
Last Modified: 21 Apr 2016 12:34
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