Search

Image segmentation is an important part of image understanding and one of the most difficult problems in image processing. For image segmentation processing, this paper proposes an image segmentation algorithm for multilevel thresholding based on novelty harmony search algorithm. Firstly, the central harmony and central congestion distance are introduced to reduce local aggregation of initial points and expand the search range. Secondly, the new harmony generation strategy is constructed, which is based on dominant harmony learning experience. Then the search space constraints and parameters adaptive adjustment are adopted to improve the search efficiency. Finally, the harmony memory updating rules are designed to enhance the diversity of population. The image segmentation effect is evaluated by the between-class variance, peak signal-to-noise ratio and mean structural similarity. A series of experiments have been carried out to analyze the segmentation effect of the proposed NHS algorithm based on the Berkeley segmentation database. Compared with the basic harmony search algorithm, improved harmony search algorithm, global best harmony search algorithm, particle swarm optimization algorithm and artificial bee colony algorithm, the experimental results show the effectiveness of the proposed algorithm. In particular the proposed algorithm is superior to other methods when the threshold number increases. The influence of noise and artifact on image segmentation is also discussed and analyzed. It illustrates that the image can be segmented in the Gaussian noise, mixed noise and strip line artifact conditions based on the proposed algorithm. [ABSTRACT FROM AUTHOR]