scholarly journals Maximum Entropy Image Segmentation Method Based On Improved Firefly Algorithm

2019 ◽  
Vol 1213 ◽  
pp. 032023
Author(s):  
Qianru Liu ◽  
Zhanjun Jiang ◽  
Haoqiang Shi
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Firefly Algorithm ◽  
Segmentation Method ◽  
Improved Firefly Algorithm

Application of Opposition Based Improved Firefly Algorithm in Multi-Level Image Thresholding of Color Images using Between Class Variance

2019 ◽  
Vol 12 ◽  
Author(s):  
Abhay Sharma ◽  
Rekha Chaturvedi ◽  
Umesh Dwivedi ◽  
Sandeep Kumar
Keyword(s):  
Image Segmentation ◽  
Firefly Algorithm ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Image Thresholding ◽  
Opposition Based Learning ◽  
Improved Firefly Algorithm ◽  
Engineering Problems ◽  
Multi Level

Background: Image segmentation is the fundamental step in image processing. Multi-level image segmentation for color image is a very complex and time-consuming process which can be defined as non-deterministic optimization problem. Nature inspired meta-heuristics are best suited to solve such problems. Though several algorithms exist; a modification to suit certain class of engineering problems is always welcome. Objective: This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function. Method: Numerous benchmark images are tested for evaluating the performance of proposed method. Results: The Experimental results validate the performance of Opposition based improved firefly algorithm (OBIFA) for multi-level image segmentation using peak signal to noise ratio (PSNR) and structured similarity index metric (SSIM)parameter. Conclusion: The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.

The Chromatic Aberration 2-D Entropy Threshold Segmentation Method Based on Adaptive Step-Length Firefly Algorithm

2019 ◽  
Vol 12 (2) ◽  
pp. 130-137
Author(s):  
Wu Shaofeng ◽  
Tong Yifei ◽  
Liu Jiafeng ◽  
Tan Qingmeng ◽  
Li Dongbo
Keyword(s):  
Firefly Algorithm ◽  
Maximum Entropy Principle ◽  
Chromatic Aberration ◽  
Step Length ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Complex Image ◽  
Improved Firefly Algorithm ◽  
Entropy Principle ◽  
Adaptive Step

Background: To effectively solve the segmentation problem with multi-target complex image, the chromatic aberration 2-D entropy threshold segmentation method based on Adaptive Step- Length Firefly Algorithm (ASLFA) is proposed in this paper. Methods: Firstly, the significance of image entropy value is analyzed and the threshold segmentation is proposed with maximum entropy principle. Then, in order to solve the problem of large amount and longtime of calculation in the threshold segmentation process, the improved firefly algorithm (FA) is proposed replacing the fixed step-length with adaptive step-length. Results: Finally, in order to make full use of the image information, the space distance of chromatic aberration is introduced and combined with FA. Conclusion: Contrast test of the proposed method and 2-D entropy threshold based on standard firefly algorithm (SFA) and genetic algorithm (GA) proves that the proposed method can improve the segmentation accuracy while ensuring the segmentation speed, and is suitable for fast and effective segmentation of multi-target images and complex images.

Implementation and Evaluation of the Image Segmentation Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1314-1317
Author(s):  
Li Jun Chen ◽  
Yong Jie Ma
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Entropy Method ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
And Performance ◽  
Standard Genetic Algorithm ◽  
Better Than

In order to achieve better image segmentation and evaluate the segmentation algorithm, a segmentation method based on 2-D maximum entropy and improved genetic algorithm is proposed in this paper, and the ultimate measurement accuracy criterion is adopted to evaluate the performance of the algorithm. The experimental results and the evaluation results show that segmentation results and performance of the proposed algorithm are both better than the segmentation method based on 2-D maximum entropy method and the standard genetic algorithm. The segmentation of the proposed algorithm is complete and spends less time; it is an effective method for image segmentation.

scholarly journals An improved method of microscopic image segmentation

2018 ◽  
Vol 176 ◽  
pp. 01041
Author(s):  
Zhang Feng Shou ◽  
Dong Fang ◽  
Liu Jian Ting ◽  
Meng Xin
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Microscopic Image ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Improved Method ◽  
Image Optimization ◽  
Segmentation Image

In order to improve the effectiveness and accuracy of image processing in modern medical inspection, a segmentation image optimization algorithm of improved two-dimensional maximum entropy threshold based on genetic algorithm combined with mathematical morphology is proposed, in view of the microscopic cell images characteristic and the shortcomings of the traditional segmentation algorithm. Through theoretical analysis and contrast test, the segmentation method proposed is superior to the traditional threshold segmentation method in microscopic cell images, and the average segmentation time of the improved algorithm is 73% and 44% higher than the traditional two-dimensional maximum entropy threshold and the improved two-dimensional maximum entropy threshold.

Crop Disease Leaf Image Segmentation Based on Genetic Algorithm and Maximum Entropy

2015 ◽  
Vol 713-715 ◽  
pp. 1670-1674 ◽  
Author(s):  
Ming Gang Du ◽  
Shan Wen Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Fitness Function ◽  
Convergence Criterion ◽  
Segmentation Method ◽  
Uniform Illumination ◽  
Crop Disease ◽  
Main Spot ◽  
Leaf Image

Crop disease leaf image segmentation is a key step in crop disease recognition. In the paper, a segmentation method of crop disease leaf image is proposed to segment leaf image with non-uniform illumination based on maximum entropy and genetic algorithm (GA). The information entropy is regarded as the fitness function of GA, the maximum entropy as convergence criterion of GA. After genetic operation, the optimal threshold is obtained to segment the image of disease leaf. The experimental results of the maize disease leaf image show that the proposed method can select the threshold automatically and efficiently, and has an advantage over the other three algorithms, and also can reserve the main spot features of the original disease leaf image.

scholarly journals Multilevel Image Segmentation Based on an Improved Firefly Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Kai Chen ◽  
Yifan Zhou ◽  
Zhisheng Zhang ◽  
Min Dai ◽  
Yuan Chao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Firefly Algorithm ◽  
The Other ◽  
Optimal Algorithms ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Firefly Algorithm ◽  
Cauchy Mutation

Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA) is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy) are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

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