An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

Abstract

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

“…The one-dimensional information entropy of an image can show the concentration characteristics of the grey-scale distribution in the image, but it cannot reflect the spatial characteristics of the grey-scale distribution. On the basis of the one-dimensional entropy, characteristic quantities which can reflect the spatial characteristics of the grey-scale distribution can be introduced to compose the two-dimensional entropy of an image [35]. The neighbourhood grey mean of the image is chosen as the spatial characteristic quantity of the grey scale distribution, and combated with the pixel grey scale of the image to form into the feature binary (i,j).…”

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

“…Further, HRO algorithm. May be combined with another local search mechanism and existing chaotic strategy 31 Chaotic Lightning Attachment Procedure Optimization (CLAPO) 2D Maximum entropy Liu et al ( 2020b ) Standard Gray Scale Images Proposed method is compared with ALO, GOA, SCA, WOA, LAPO, FWA and GA CPU Time, Maximum fitness value, Average fitness value, Excellent Rate and Iterations consumed The proposed CLAPO claims better outcome when compared to the other algorithms in terms of efficiency, performance and segmentation effect. However, compared to GA, SCA proposed algorithm is slower in terms of running speed that needs further attention and improvisation 32 Improved Thermal Exchange Optimization based GLCM (GLCM-ITEO) Diagonal Class Entropy (DCE) and Otsu thresholding Xing and Jia ( 2019 ) Color Natural Images, Satellite Images and Berkeley Images Proposed method is compared with GLCM-PSO, GLCM-BA, GLCM-FPA, GLCM-ITEO, GLCM-CSA, Otsu-CSA, Otsu-BA, Otsu-FPA, Otsu-ITEO and Otsu-PSO PRI, VoI, GCE, CPU Time and BDE The proposed GLCM-ITEO algorithm is better in terms of segmentation ability and CPU time.…”

A Novel Modified Lightning Attachment Procedure Optimization Technique for Optimal Allocation of the FACTS Devices in Power Systems

“…Liu, et. al [44] optimize the image segmentation using LAPO technique. The conventional LAPO may apt to trap into the local optima, so a modified MLAPO is proposed to solve the stagnation of LAPO.…”