Multi-Level Image segmentation based on Fuzzy - Tsallis Entropy and Differential Evolution (original) (raw)
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A Fuzzy Entropy Based Multi-Level Image Thresholding Using Differential Evolution
International Conference on Swarm, Evolutionary and Memetic Computing, 2014
This paper presents a multi-level image thresholding approach based on fuzzy partition of the image histogram and entropy theory. Here a fuzzy entropy based approach is adopted in context to the multi-level image segmentation scenario. This entropy measure is then optimized to obtain the thresholds of the image. In order to solve the optimization problem, a meta-heuristic, Differential Evolution (DE) is used, which leads to a faster and accurate convergence towards the optima. The performance of DE is also measured with respect to some popular global optimization techniques like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs).The outcomes are compared with Shannon entropy, both visually and statistically in order to establish the perceptible difference in image.
Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy
Image and Vision Computing, 2019
This paper presents a multilevel image thresholding approach which relies on Tsallis entropy using Fuzzy partition with a novel threshold selection technique. In order to compute the optimal threshold values, Differential Evolution (DE) has been employed. The proposed method can further be exploited in image segmentation which is considered to be a critical step in image processing. Our proposed threshold selection technique is based on Tsallis-Fuzzy entropy and the results are compared with Shannon entropy (or fuzzy entropy) and Tsallis entropy based existing threshold selection techniques. The experiments are performed on two different sets of images and the results have been compared with that of existing state-of-the-art methods, namely, Patch Levy Bees' Algorithm (PLBA), Bacterial Foraging optimization (BFO), modified Bacterial Foraging optimization (MBFO) and Bees' Algorithm (BA). Quantitative analysis is carried out based on three image quality metrics viz SSIM, PSNR and SNR. Standard deviation and CPU time for convergence of the objective function have been calculated for performance evaluation. Furthermore, the statistical significance of our method has been estimated using Friedman test and Wilcoxon test. The experimental results manifest that our method produces results superior to the methods in comparison.
Multilevel Image Thresholding Based on Tsallis Entropy and Differential Evolution
Lecture Notes in Computer Science, 2012
Image segmentation is known as one of the most critical task in image processing and pattern recognition in contemporary time, for this purpose Multi Level Thresholding based approach has been an acclaimed way out. Endeavor of this paper is to focus on obtaining the optimal threshold points by using Tsallis Entropy. In this paper, we have incorporated a Differential Evolution (DE) based technique to acquire optimal threshold values. Furthermore, results are compared with two state-of-art algorithms-a. Particle Swarm Optimization (PSO), and b. Genetic Algorithm (GA). Several image quality assessment indices are applied for the performance analysis of the outcome derived by applying the proposed algorithm.
Entropy-based Multilevel 2D Histogram Image Segmentation using DEWO Optimization Algorithm
2019 International Conference on Automation, Computational and Technology Management (ICACTM), 2019
Thresholding is widely used image segmentation technique in many real-life applications like document image processing, quality inspection to detect defective parts of machines, medical imaging etc. Multilevel image segmentation is a simple approach for colored image segmentation with less computational complexity, but multilevel image segmentation is not able to properly exploit the spatial correlation of image's pixels. This study proposes a hybrid of Differential Evolution and Whale Optimization (DEWO) for entropy based multilevel image segmentation using non-local means 2Dhistogram and to perform colored image segmentation. The proposed approach is compared with some prominent meta heuristic algorithms in recent past using Tsallis entropy, Renyi entropy, and Kapur entropy functions to validate its efficiency for different entropy functions. Results obtained from the proposed approach for image segmentation is better than all the other meta-heuristic algorithms in every entropy-based segmentation performed.
Entropy, 2021
Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metahe...
Springer, 2017
Multilevel thresholding is one of the most broadly used approaches to image segmentation. However, the traditional techniques of multilevel thresholding are time-consuming, especially when the number of the threshold values is high. Thus, population-based metaheuristic (P-metaheuristic) algorithms can be used to overcome this limitation. P-metaheuristic algorithms are a type of optimization algorithms, which improve a set of solutions using an iterative process. For this purpose, image thresholding problem should be seen as an optimization problem. This paper proposes multilevel image thresholding for image segmentation using several recently presented P-metaheuristic algorithms, including whale optimization algorithm, grey wolf optimizer, cuckoo optimization algorithm, biogeography-based optimization, teaching–learning-based optimization, gravitational search algorithm, imperialist competitive algorithm, and cuckoo search. Kapur’s entropy is used as the objective function. To conduct a more comprehensive comparison, the mentioned P-metaheuristic algorithms were compared with five others. Several experiments were conducted on 12 benchmark images to compare the algorithms regarding objective function value, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability. In addition, Friedman test and Wilcoxon signed rank test were carried out as the nonparametric statistical methods to compare P-metaheuristic algorithms. Eventually, to create a more reliable result, another objective function was evaluated based on Cross Entropy.
A Differential Evolution Approach to Multi-level Image Thresholding using Type II Fuzzy Sets
Springer International Publishing (SEMCCO 2013), 2014
Multi-level image thresholding is an important aspect in many image processing and computer vision applications. In the last decade, many fuzzy based image thresholding techniques have been proposed. In this article a new method for multi-level image thresholding is proposed using Type II Fuzzy sets. A new entropy measure is defined which is maximized to obtain the optimal thresholds for an image. As the number of thresholds increases, exhaustive search appears to be very time consuming. So, Differential Evolution (DE), a meta-heuristic algorithm, is used for fast selection of optimal thresholds. The proposed algorithm is compared with a fuzzy entropy based algorithm using image quality assessment measures Feature Similarity Index Measurement (FSIM) and Gradient Similarity Measurement (GSM). The use of DE is also justified by comparing it with other modern state-of-art algorithms like Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Fuzzy Entropy Based Approach to Image Thresholding
International Journal of Software Engineering and Knowledge Engineering
Image thresholding plays very important role in many computer vision and image processing applications. Segmentation based on gray level histogram thresholding consists of a method that divides an image into two regions of interest; object and background. In image processing, we deal with many ambiguous situations. Fuzzy set theory is a useful mathematical tool for handling the ambiguity or uncertainty and provides a new tool to deal with multimodal histograms. In this paper, a novel image thresholding approach is proposed using fuzzy entropy. In the proposed approach, at first the input image is preprocessed to reduce noise without any loss of image details using fuzzy set theoretic approach. Then an optimal threshold is obtained from the preprocessed image using fuzzy entropy. The improvement of the proposed approach is discussed with the help of experimental results on different types of test images.
Recursive algorithm based on fuzzy 2-partition entropy for 2-level image thresholding
Pattern Recognition, 2005
The fuzzy c-partition entropy approach for threshold selection behaves well in segmenting images. But the size of search space increases very rapidly when the number of parameters needed to determine the membership function increases. The computation complexity of the fuzzy 2-partition entropy approach is bounded by O(L 3). In this paper, a recursive scheme which decreases the computation complexity of the basic algorithm to O(L 2) is proposed. The approach does not need the calculation of the membership function. The processing time of each image is reduced from more than 5 min to less than 20 s.
Thresholding Algorithms for Image Segmentation - Entropy Based Comparison
Global Journal of Enterprise Information System, 2019
Purpose: Image segmentation relates to the process of labelling of pixels in any image. This concept of image segmentation is generally used to find the Region of Interest – ROI in images, thus it is used very frequently in the field of computer vision. Among the various techniques of image segmentation, Thresholding is quite common, this technique is quite simple and efficient. Thresholding can be done locally as well as globally, and this selection of suitable thresholding technique is a critical factor for image segmentation. The Otsu’s method and K-means algorithm are commonly used techniques for thresholding, the Otsu’s method works on Global thresholding and the K-means works on Local thresholding. But both methods i.e. Otsu’s method and K-means algorithm, explores the criteria of minimizing the within-class variance, to yield better segmentation results. But among the two, which one is better? The work performed in this paper relates to the comparison of the Otsu’s method and...