A self-adaptive multi-objective harmony search based fuzzy clustering technique for image segmentation

Image segmentation can be considered as a problem of clustering since the pixels in the digital image are clustered in term of some evaluation criteria. Generally, clustering technique in image segmentation employs a single objective which can not reach ideal result for various kinds of images. Moreover, fuzzy c-means (FCM) algorithms which determine the fuzzy partition matrix of the data set by solving the clustering problem with conditional constraints and obtain the clustering output, have been verified effective and efficient for image segmentation. In fact, these FCM algorithms still have some shortcomings including: being sensitive to outliers and noise, key parameters need to be adjusted with experience. In view of this, a self-adaptive multi-objective harmony search based fuzzy clustering (SAMOHSFC) technique for image segmentation is proposed in this paper. SAMOHSFC technique encodes several cluster centers in one harmony vector and optimizes multiple objectives. In addition, we consider the spatial information of the image as an attribute of the input data set besides the attribute of gray information of input image in the SAMOHSFC. Superiority of the proposed algorithm over three classic segmentation algorithms has been verified for a synthetic and two real images from quantitative and visual aspect. In the experiment, the effect of different kinds of spatial information on the segmentation performance of the SAMOHSFC is analyzed. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.