TY - JOUR
T1 - A multi-threshold segmentation approach based on artificial bee colony optimization
AU - Cuevas, Erik
AU - Sención, Felipe
AU - Zaldivar, Daniel
AU - Pérez-Cisneros, Marco
AU - Sossa, Humberto
N1 - Funding Information:
Acknowledgements The proposed algorithm is part of the vision system used by a biped robot supported under the grant CONACYT CB 82877. H. Sossa thanks SIP-IPN and CONACYT for the economical support under grants 20111016 and 155014, respectively.
PY - 2012/10
Y1 - 2012/10
N2 - This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is an evolutionary algorithm inspired by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. In the model, each Gaussian function represents a pixel class and therefore a threshold point. Unlike the Expectation-Maximization (EM) algorithm, the ABC method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness. The paper also includes an experimental comparison to the EM and to one gradient-based method which ultimately demonstrates a better performance from the proposed algorithm..
AB - This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is an evolutionary algorithm inspired by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. In the model, each Gaussian function represents a pixel class and therefore a threshold point. Unlike the Expectation-Maximization (EM) algorithm, the ABC method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness. The paper also includes an experimental comparison to the EM and to one gradient-based method which ultimately demonstrates a better performance from the proposed algorithm..
KW - Artificial Bee Colony
KW - Automatic thresholding
KW - Image segmentation
KW - Intelligent image processing
UR - http://www.scopus.com/inward/record.url?scp=84868381657&partnerID=8YFLogxK
U2 - 10.1007/s10489-011-0330-z
DO - 10.1007/s10489-011-0330-z
M3 - Artículo
SN - 0924-669X
VL - 37
SP - 321
EP - 336
JO - Applied Intelligence
JF - Applied Intelligence
IS - 3
ER -