Research Status of Image Segmentation Based on Particle Swarm Optimization at Home and Abroad

Image segmentation is the basis of image understanding and the key step of image analysis. The data show that the more prior knowledge is used in the segmentation process, the higher the accuracy of the algorithm and the stronger the segmentation ability, but the segmentation speed is slow. Aiming at the robustness and speed of image threshold segmentation, the fast segmentation technology and method based on image gray threshold are studied. The main work is to construct a new threshold segmentation model by using the concepts of grey theory, wavelet transform, fuzzy theory, pattern recognition, entropy and histogram to improve the segmentation quality. On the other hand, the swarm intelligence algorithm, which Chinese scholars began to pay attention to in the 20th century, is used to optimize the segmentation model and improve the segmentation speed through its efficient and parallel optimization ability. The main research results include: (1) A fast SAR image segmentation method based on two-dimensional gray entropy model is proposed by combining genetic algorithm, wavelet transform, image two-dimensional entropy and gray theory. Theoretical analysis and experimental results show that compared with the traditional Abutaleb segmentation method, this method is robust and the segmentation speed is obviously accelerated. (2) Applying Tsallis entropy to image threshold segmentation, a fast SAR image segmentation method based on gray Tsallis entropy is proposed by using the parallel optimization ability of particle swarm optimization algorithm. This method is more flexible and faster than the traditional image segmentation method. (3) Combining fuzzy theory and dichotomy correlation analysis theory, a fast SAR image segmentation method based on gray fuzzy entropy is proposed. This method makes up for the sensitivity of traditional fuzzy segmentation methods to noise, enhances robustness, and improves the segmentation speed after optimization by particle swarm optimization algorithm. (4) The Fisher criterion function in pattern recognition theory is studied and used as the selection criterion of image threshold, and a fast image segmentation method based on Fisher criterion and gray post-processing is proposed. On the one hand, this method reduces the influence of boundary information on segmentation results, on the other hand, it improves the search speed of threshold and reduces the segmentation time with the help of particle swarm optimization algorithm.