Abstract
Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.
Highlights
With the rapiddevelopment and application of computers, handheld terminals, networks, and multimedia technologies, multimedia data have gained more and more widespread applications, especially the explosive growth of image and video data [1]
We summarize the contributions of this work as follows: (1) to improve the effectiveness of multithreshold image segmentation, we use the improved cuckoo search (ICS) algorithm to solve problem; (2) we propose a new method called ICS, which improves the convergence speed and accuracy of the ICS-based method by combining advanced optimization strategies to improve image segmentation; (3) we present the detailed design and implementation of ICS and compare it with modern metaheuristic algorithms such as cuckoo search (CS), Ant Clony Optimization (ACO), and particle swarm optimization (PSO). e experimental results show that the ICS algorithm can provide better performance in terms of image segmentation with multiple thresholds
E image segmentation problem can be converted into a multi-dimensional function problem, which is essentially a multi-objective optimization problem, and a large number of experiments have proved that the cuckoo algorithm has a strong global convergence ability and is better than other group intelligent optimization algorithms in solving the optimal value of the function of the local explosive; the use of Levy behavior makes the individual jump out of the local optimal solution, so this paper will use the bionic cuckoo algorithm for image segmentation to convert the problem of maximum entropy threshold objective function to segment the optimal threshold of an image into the problem of finding the optimal value of the fitness function
Summary
With the rapiddevelopment and application of computers, handheld terminals, networks, and multimedia technologies, multimedia data have gained more and more widespread applications, especially the explosive growth of image and video data [1]. Image segmentation is one of the most important tools in graphics processing methods, which has a wide range of applications in target recognition, feature detection, image annotation, and semantic search. Erefore, in this paper, we choose the cuckoo search (CS) [5] for image segmentation, which is one of the relatively new metaheuristics to efficiently solve the optimization problem by simulating the parasitic brood (Brood Parasitism) of some species of cuckoo, which uses the Levy flight search mechanism to improve the performance of the algorithm.
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