Abstract
Image segmentation is one of the key steps of target recognition. In order to improve the accuracy of image segmentation, an image segmentation algorithm combining Pulse Coupled Neural Network(PCNN) and adaptive Glowworm Algorithm(GA) is proposed. The algorithm retains the advantages of the GA. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value, and takes the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the GA. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with TDE algorithm, 9.22% compared with GA algorithm, and 22.58% compared with AUTO algorithm.
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