A comparison of nature inspired algorithms for multi-threshold image segmentation

Authors:
ValentıN Osuna-Enciso;Erik Cuevas;Humberto Sossa
Affiliations:
Centro de Investigación en Computación-IPN, Av. Juan de Dios Batiz S/N, Col. Nueva Industrial Vallejo, Mexico, D.F., Mexico;Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal., Mexico;Centro de Investigación en Computación-IPN, Av. Juan de Dios Batiz S/N, Col. Nueva Industrial Vallejo, Mexico, D.F., Mexico
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.