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Improved glowworm swarm optimization algorithm applied to multi-level thresholding

Image segmentation is considered an important basic task in the analysis and understanding of images. The process of image segmentation involves the partitioning of an image into multiple regions, i.e., each pixel in the image is assigned a label that corresponds to certain visual characteristics. Therefore, image segmentation is widely used for image processing tasks such as classification and object recognition. The thresholding process, however, is an NP-hard problem since n thresholds need to be found and each threshold can take numbers from 0 to 255 according to the intensity values. Thus, exact methods are only feasible for a small number of thresholds. Nature-inspired algorithms have recently been applied to this hard optimization problem and shown very good results. In particular, many swarm intelligence methods have been applied to multi-level thresholding. This paper uses a glowworm swarm optimization (GSO) approach and further enhances it in order to improve the accuracy of the thresholding as well as to improve the robustness of the results. The proposed approach makes adjustments to the local decision radius, the selection method, and the step size. The proposed GSO algorithm shows improved results in particular for larger numbers of thresholds as compared to the basic GSO algorithm.