  The goal of this paper is to present a new efficient image segmentation
method based on evolutionary computation which is a model inspired from human
behavior. Based on this model, a four layer process for image segmentation is
proposed using the split/merge approach. In the first layer, an image is split
into numerous regions using the watershed algorithm. In the second layer, a
co-evolutionary process is applied to form centers of finals segments by
merging similar primary regions. In the third layer, a meta-heuristic process
uses two operators to connect the residual regions to their corresponding
determined centers. In the final layer, an evolutionary algorithm is used to
combine the resulted similar and neighbor regions. Different layers of the
algorithm are totally independent, therefore for certain applications a
specific layer can be changed without constraint of changing other layers. Some
properties of this algorithm like the flexibility of its method, the ability to
use different feature vectors for segmentation (grayscale, color, texture,
etc), the ability to control uniformity and the number of final segments using
free parameters and also maintaining small regions, makes it possible to apply
the algorithm to different applications. Moreover, the independence of each
region from other regions in the second layer, and the independence of centers
in the third layer, makes parallel implementation possible. As a result the
algorithm speed will increase. The presented algorithm was tested on a standard
dataset (BSDS 300) of images, and the region boundaries were compared with
different people segmentation contours. Results show the efficiency of the
algorithm and its improvement to similar methods. As an instance, in 70% of
tested images, results are better than ACT algorithm, besides in 100% of tested
images, we had better results in comparison with VSP algorithm.
