Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm
XIAO Leyi① OUYANG Honglin① FAN Chaodong②
①(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China) ②(College of Information Engineering, Xiangtan University, Xiangtan 411105, China)
Abstract：The threshold value of Q in the post process of traditional cross section projection Otsu’s method is a constant, which is not universal applicability for images with different noises. To solve this problem, this paper proposes a multi-objective cross section projection Otsu's method based on memory knetic-molecular theory ptimization algorithm. Based on the maximum between-class variance criterion and the maximum Peak Signal to Noise Ratio (PSNR) criterion, a multi-objective image segmentation model is established to take into account the segmentation accuracy and anti-noise capability for image segmentation by combining threshold Q with segmentation threshold T. In order to improve the efficiency of the algorithm, a memory knetic-molecular theory optimization algorithm is proposed for the multi-objective cross section projection Otsu's method by introducing the artificial memory principles into knetic-molecular theory optimization algorithm. The experimental results show that this method has significant advantages in segmentation accuracy, anti-noise capability and robustness, and is more universal applicability for images with different noises.
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