IMAGE THRESHOLDING USING FUZZY CORRELATION CRITERION AND HARMONY SEARCH ALGORITHM

2014 ◽  
Vol 13 (01) ◽  
pp. 1450003 ◽  
Author(s):  
FANGYAN NIE ◽  
JIANQI LI ◽  
TIANYI TU ◽  
MEISEN PAN
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Experimental Studies ◽  
Search Time ◽  
Harmony Search Algorithm ◽  
Image Thresholding ◽  
Member Function ◽  
Fuzzy Correlation ◽  
Correlation Criterion ◽  
High Degree

This paper reports a novel image thresholding method based on fuzzy set theory and maximum correlation criterion using harmony search algorithm. In this study, the maximum fuzzy correlation criterion is defined using Z- and S- fuzzy member function on image gray level histogram. Then fuzzy correlation criterion image segmentation based on harmony search algorithm is implemented. The experimental studies were conducted on a variety of images by testing the proposed method and some classical thresholding methods. The experimental results demonstrate that the proposed method can select the threshold automatically and effectively. Compared with the exhaustive search method, the harmony search algorithm can give high degree of accuracy while needing less search time and has good search stability in the segmentation experiments.

scholarly journals Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

2018 ◽  
Vol 8 (6) ◽  
pp. 4593 ◽  
Author(s):  
Erwin Erwin ◽  
Saparudin Saparudin ◽  
Wulandari Saputri
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Multilevel Thresholding ◽  
Improved Harmony Search ◽  
High Degree ◽  
Improved Harmony Search Algorithm

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.

Optimal Power Flow Solution Using the Harmony Search Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1938-1941
Author(s):  
Ping Ren ◽  
Nan Li
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Time ◽  
Mathematical Optimization ◽  
Harmony Search Algorithm ◽  
Optimal Power

Inthis paper, the nonlinear optimal control problem is formulated as amulti-objective mathematical optimization problem. Harmony search (HS)algorithm is one of the new heuristic algorithms. The harmony search(HS) optimization algorithm is introduced forthe first time in solving the optimal power flow(OPF) solution. A case onoptimal power flow problem in the IEEE 30 bus system is presented to show themethodology’s feasibility and efficiency, compared with the existing optimalpower flow problem in power system methods, the search time of the HSoptimization algorithm is shorter and the result is close to the idealsolution, simultaneously.

Optimal linear cooperation spectrum sensing method based on chaos harmony search algorithm

2013 ◽  
Vol 32 (9) ◽  
pp. 2412-2417
Author(s):  
Yue-hong LI ◽  
Pin WAN ◽  
Yong-hua WANG ◽  
Jian YANG ◽  
Qin DENG
Keyword(s):  
Spectrum Sensing ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Optimal Linear

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

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