Image Threshold Segmentation Based on An Improved Bee Colony Algorithm

Image segmentation means that the image is divided into specific and unique regions. There are many existing image segmentation methods, and the threshold-based segmentation method is widely applied because of its easy implementation, simplicity and high efficiency. In this paper, artificial bee colony algorithm is applied to image threshold segmentation. Kapur entropy is used as a fitness function, the artificial bee colony algorithm is improved through the adaptive scaling factor. The search area is enlarged through the large-scale factor, the neighborhood search scope is reduced through the small-scale factor and the search efficiency is enhanced. Finally, by comparing the PSNR values of the image, the algorithm has a good segmentation effect and good convergence performance.