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1st International conference on complex Systems (IccS)

作者： Assas, O. Benmahammed, Kheir Univ Batna Dept Elect Ave Boukhlouf Med El Hadi Batna 05000 Algeria Univ Farhat Abbas Setif Algerie Setif Algeria

ISBN: (纸本)9781467347662

The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper we applied fuzzy entropy in image segmentation, used it to select the fuzzy region of membership function automatically so that an image can be transformed into fuzz domain with maximum fuzzy entropy, and implemented particle swarm optimization algorithm to find the optimal combination of fuzzy parameters. The proposed fast approach has been tested on many images for example the processing time of tri level thresholding of each image is reduced from more than 22 min to less than 0.5 s. The fuzzy c-partition entropy using PSO algorithm perform equally well in terms of the quality of image segmentation and leads to a good visual result.

关键词： Segmentation Thresholding fuzzy Logic Entropy fuzzy c-partition Particle Swarm Optimisation

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Threshold selection based on fuzzy c-partition entropy approach

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PATTERN REcOGNITION 1998年第7期31卷 857-870页

作者： cheng, HD chen, JR Li, JG Utah State Univ Dept Comp Sci Logan UT 84322 USA

Thresholding is an important topic for image processing, pattern recognition and computer vision. Selecting thresholds is a critical issue for many applications. The fuzzy set theory has been successfully applied to many areas, such as control, image processing, pattern recognition, computer vision, medicine, social science, etc. It is generally believed that image processing bears some fuzziness in nature. In this paper, we use the concept of fuzzy c-partition and the maximum fuzzy entropy principle to select threshold values for gray-level images. We have conducted experiments on many images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively, and the resulting images can preserve the main features of the components of the original images very well. (c) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： thresholding fuzzy logic fuzzy c-partition maximum entropy principle simulated annealing

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Differential Evolution and Particle Swarm Optimization in fuzzy c-partition

Differential Evolution and Particle Swarm Optimization in Fu...

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International conference on Multimedia computing and Systems (IcMcS)

作者： Assas, Ouarda Univ Msila Dept Comp Sci Msila Algeria Univ Msila LASS Msila Algeria

ISBN: (纸本)9781479938247

The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper, the selection of thresholds (fuzzy parameters) was seen as an optimization problem and solved using particle swarm optimization (PSO) and differential evolution (DE) algorithms. The proposed fast approaches have been tested on many images. For example, the processing time of four-level thresholding using both PSO and DE is reduced to less than 0.4s. PSO and DE show equal performance when the number of thresholds is small. When the number of thresholds is greater, PSO algorithm performs better than DE in terms of precision, robustness and execution time.

关键词： component Entropy Histograms Image segmentation Optimization Particle swarm optimization partitioning algorithms Pattern recognition Thresholding fuzzy c-partition Differential Evolution Algorithm

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Image Thresholding Using Type-2 fuzzy c-partition Entropy and Particle Swarm Optimization Algorithm

Image Thresholding Using Type-2 Fuzzy C-Partition Entropy an...

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International conference on computer Vision and Image Analysis Applications (IccVIA)

作者： Assas, Ouarda Univ Msila Dept Comp Sci LASS Msila Algeria

ISBN: (纸本)9781479971862

The imprecision in an image can be expressed in terms of ambiguity of belonging of a pixel in the image or the bottom (if it is black or white), or at the in-definition of the form and the geometry of a region in the image, or the combination of the two previous factors. The fuzzy c-partition entropy approach for threshold selection is one of the best image thresholding techniques, but its complexity increases with the number of thresholds. In this paper, a multi-level thresholding method for image segmentation using type-2 fuzzy c-partition entropy is presented. Type-2 fuzzy sets represent fuzzy sets with fuzzy membership values. The procedure for finding the optimal combination of all the fuzzy parameters is implemented by a particle swarm optimization algorithm. Experimental results reveal that the proposed image thresholding approaches has good performances for images with low contrast and grayscale ambiguity.

关键词： Segmentation Thresholding fuzzy Logic Type-2 fuzzy Sets Entropy fuzzy c-partition Particle Swarm Optimisation

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Probability Logic Reasoning with Threshold-Surety Based on fuzzy c-partition Entropy Approach

Probability Logic Reasoning with Threshold-Surety Based on F...

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The 2010 2nd International conference on Future computer and communication(第二届未来计算机与通信国际会议 IcFcc 2010)

作者： Yong Li Weiyi Liu Department of Computer Science and Engineering Yunnan University Kunming P.R.China

ISBN: (纸本)9781424458219;9781424458240

We propose a method of probability logic reasoning with threshold-surety to the problem of making inference from a knowledge base in experts system, which treats a finite set of premises in the knowledge base as assertions that are true with probability at least some primary threshold, and consider as a finite set of alternative conclusions those assertions probabilistically entailed to have probability at least some suitable threshold. Based on comparative probability theory, we construct a probability entailment relation between premises and conclusions, and use the method of fuzzy c-partition and the maximum fuzzy entropy principle to select the threshold values. An illustrative example of application of our method shows how we make inferences from knowledge base.

关键词： probability logic reasoning threshold-surety fuzzy c-partition maximum entropy principle

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A technique of three-level thresholding based on probability partition and fuzzy 3-partition

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2001年第3期9卷 469-479页

作者： Zhao, MS Fu, AMN Yan, H Univ Sydney Sch Elect & Informat Engn Imaging Sci & Engn Lab Sydney NSW 2006 Australia

Thresholding is a commonly used technique in image segmentation. Selecting the correct thresholds is a critical issue. In this paper, the relationship between a probability partition (PP) and a fuzzy c-partition (FP) in thresholding is given. This relationship and the entropy approach are used to derive a thresholding technique to select the best fuzzy c-partition. The measure of the selection quality is the compatibility between the FP and the PP generated by the problem. An entropy function defined by the PP and FP is used to measure the compatibility. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient algorithm for three-level thresholding is deduced. Experiments to verify the efficiency of the proposed method and comparison to some existing techniques are also presented. The experiment results show that our proposed method gives the best performance in three-level thresholding using fuzzy c-partition.

关键词： entropy fuzzy c-partition image processing probability partition thresholding

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A fast recursive algorithm based on fuzzy 2-partition entropy approach for threshold selection

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NEUROcOMPUTING 2011年第17期74卷 3072-3078页

作者： Tang, Yinggan Mu, Weiwei Zhang, Ying Zhang, Xuguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Natl Lighting Test Ctr Beijing 100022 Peoples R China

The fuzzy c-partition entropy approach for threshold selection is an effective approach for image segmentation. The approach models the image with a fuzzy c-partition, which is obtained using parameterized membership functions. The ideal threshold is determined by searching an optimal parameter combination of the membership functions such that the entropy of the fuzzy c-partition is maximized. It involves large computation when the number of parameters needed to determine the membership function increases. In this paper, a recursive algorithm is proposed for fuzzy 2-partition entropy method, where the membership function is selected as S-function and Z-function with three parameters. The proposed recursive algorithm eliminates many repeated computations, thereby reducing the computation complexity significantly. The proposed method is tested using several real images, and its processing time is compared with those of basic exhaustive algorithm, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (AcO) and simulated annealing (SA). Experimental results show that the proposed method is more effective than basic exhaustive search algorithm, GA, PSO, AcO and SA. crown copyright (c) 2011 Published by Elsevier B.V. All rights reserved.

关键词： Image segmentation fuzzy c-partition Thresholding Recursive algorithm Entropy

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An expert system based on fuzzy entropy for automatic threshold selection in image processing

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EXPERT SYSTEMS WITH APPLIcATIONS 2009年第2期36卷 3077-3085页

作者： Avci, Engin Avci, Derya Firat Univ Dept Elect & Comp Sci TR-23119 Elazig Turkey

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted oil many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and charvat entropy (MFHcE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHcE methods oil selecting the 2-level threshold value automatically and effectively. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： Expert system Maximum fuzzy entropy fuzzy c-partition Sure entropy Shannon entropy Havrada and charvat entropy Automatic threshold selection

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fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation

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fuzzy SETS AND SYSTEMS 2005年第3期150卷 561-577页

作者： Hung, WL Yang, MS Natl Hsinchu Teachers Coll Dept Math Educ Hsinchu 30014 Taiwan Chung Yuan Christian Univ Dept Appl Math Chungli Taiwan

This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFcN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and influence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFcN clustering algorithm is more robust than the fuzzy c-numbers (FcN) clustering algorithm presented by Yang and Ko (fuzzy Sets and Systems 84 (1996) 49). Some numerical experiments were performed to assess the performance of FcN and AFcN. Numerical results clearly indicate AFcN to be superior in performance to FcN. Finally, we apply the FcN and AFcN algorithms to real data. The experimental results show the superiority of AFcN in Taiwanese tea evaluation. (c) 2004 Elsevier B.V. All rights reserved.

关键词： alternative fuzzy c-numbers clustering fuzzy clustering fuzzy c-partition fuzzy c-numbers clustering LR-type fuzzy numbers

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On an Approach to clustering of Network Traffic

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AUTOMATIc cONTROL AND cOMPUTER ScIENcES 2007年第2期41卷 107-113页

作者： Kerimova, L. E. Natl Acad Sci Azerbaijan Inst Informat Technol Ul F Agaeva 9 AZ-1141 Baku Azerbaijan

The problem of clustering with a new generalized performance criterion is considered, the concept of the "center of the cluster" is introduced, and it is shown that the definition of the concept is well-defined. Necessary conditions for minimization of the functional are derived in a theorem which encompasses both fuzzy and crisp partitions into clusters. The k-means algorithm, which is based on this necessary condition, finds the optimal cluster iteratively.

关键词： classification clustering objective function performance criterion performance functional crisp c-partition fuzzy c-partition k-means algorithm

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