Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi

Yıl 2018, , 841 - 847, 01.12.2018

Ismail Koc , Omer Kaan Baykan Ismail Babaoglu

https://doi.org/10.2339/politeknik.389613

Cited By: 3

Öz

Çoklu eşikleme, görüntü işleme ve
örüntü tanıma için önemli bir görüntü işleme tekniğidir. Optimal bir eşik
değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir. İki seviye
segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı
içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir. Özellikle çok
seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı
tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır. Zor problemler
için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak
bilinmektedir.  Bu çalışmada, doğadaki
gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son
zamanlarda önerilmiş sürü tabanlı meta sezgisel olan gri kurt algoritması (GWO)
çok seviyeli görüntü eşikleme probleminin çözümü için kullanılmaktadır.
Standart test resimleri üzerinde yapılan deneysel sonuçlar GWO algoritmasının
diğer modern algoritmalarla karşılaştırılabilir olduğunu göstermektedir.

Anahtar Kelimeler

Çok seviyeli imge eşikleme, otsu metodu, sürü zekası, optimizasyon algoritmaları, gri kurt algoritması

Multilevel Image Thresholding Selection Based on Grey Wolf Optimizer

Öz

ABSTRACT

Multilevel thresholding is an important image process technique for
image processing and pattern recognition. Selecting an optimal threshold value
is one of the most crucial phase in image thresholding. While bi-level
segmentation contains separating the original image into subdivided sections
with help of a threshold value, multilevel segmentation involves multi
threshold values. Especially in multilevel image tresholding, the computational
time of detailed search increases exponentially with the number of preferred
thresholds. For compelling problems, swarm intelligence is known as one of the
successful and influential optimization methods. In this paper, the grey wolf
optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates
the social leadership and hunting behavior of gray wolves in nature is employed
for solving the multilevel image thresholding problem. The experimental results
on standard benchmark images indicate that the grey wolf optimizer algorithm is
comparable with other state of the art algorithms.

Anahtar Kelimeler

Multilevel image thresholding, otsu method, herd intelligence, optimization algorithms, gray wolf algorithm

Kaynakça

• [1] Sathya P. and Kayalvizhi R.. "Modified bacterial foraging algorithm based multilevel thresholding for image segmentation". Engineering Applications of Artificial Intelligence.24: 595-615, (2011)
• [2] Lazaro J.. Martin J. L.. Arias J.. Astarloa A.. and Cuadrado C.. "Neuro semantic thresholding using OCR software for high precision OCR applications". Image and Vision Computing. 28: 571-578, (2010)
• [3] Anagnostopoulos G. C.. "SVM-based target recognition from synthetic aperture radar images using target region outline descriptors". Nonlinear Analysis-Theory Methods & Applications. 71: E2934-E2939, (2009)
• [4] Hsiao Y. T.. Chuang C. L.. Lu Y. L.. and Jiang J. A.. "Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames”. Image and Vision Computing. 24: 1123-1136, (2006)
• [5] Adollah R.. Mashor M. Y.. Rosline H.. and Harun N. H.. "Multilevel Thresholding as a Simple Segmentation Technique in Acute Leukemia Images”. Journal of Medical Imaging and Health Informatics. 2: 285-288, (2012)
• [6] Dominguez A. R. andNandi A. K.. "Detection of masses in mammograms via statistically based enhancement. multilevel-thresholding segmentation. and region selection". Computerized Medical Imaging and Graphics. 32: 304-315, ( 2008)
• [7] Alihodzic A. and Tuba M.. "Improved bat algorithm applied to multilevel image thresholding”. ScientificWorldJournal. 2014. 176718, (2014)
• [8] Kumar S.. Kumar P.. Sharma T. K.. and Pant M.. "Bi-level thresholding using PSO. Artificial Bee Colony and MRLDE embedded with Otsu method". Memetic Computing. 5: 323-334, (2013)
• [9] Pare S.. Kumar A.. Bajaj V.. and Singh G. K.. "A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve". Applied Soft Computing. 47. 76-102, (2016)
• [10] Guo Y. Z.. Zhu W. X.. Jiao P. P.. Ma C. H.. andYang J. J.. "Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation". Biosystems Engineering. 135: 54-60, (2015)
• [11] Pal N. R. and Pal S. K.. "A Review on Image Segmentation Techniques”. Pattern Recognition. 26: 1277-1294, (1993)
• [12] Osuna-Enciso V.. Cuevas E.. and Sossa H.. "A comparison of nature inspired algorithms for multi-threshold image segmentation”. Expert Systems with Applications. 40: 1213-1219, (2013)
• [13] Ayala H. V. H.. Santos F. M. d.. Mariani V. C.. and Coelho L. d. S.. "Image thresholding segmentation based on a novel beta differential evolution approach”. Expert Systems with Applications. 42: 2136-2142, (2015)
• [14] Yang X. S.. "Efficiency Analysis of Swarm Intelligence and Randomization Techniques". Journal of Computational and Theoretical Nanoscience. 9: 189-198, (2012)
• [15] Yang X. S.. "Review of meta-heuristics and generalised evolutionary walk algorithm". International Journal of Bio-Inspired Computation. 3: 77-84, (2011)
• [16] Yang X. S.. "Free Lunch or No Free Lunch: That Is Not Just a Question?". International Journal on Artificial Intelligence Tools. 21: (2012)
• [17] Gandomi A. H. and Yang X. S.. "Evolutionary boundary constraint handling scheme". Neural Computing & Applications. 21: 1449-1462, (2012)
• [18] Kennedy J. and Eberhart R.. "Particle swarm optimization". 1995 Ieee International Conference on Neural Networks Proceedings. 1-6: 1942-1948, (1995)
• [19] Storn R. and Price K.. "Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces". Journal of Global Optimization. 11: 341-359, (1997)
• [20] Yang X. S.. "Firefly Algorithms for Multimodal Optimization". Stochastic Algorithms: Foundations and Applications. Proceedings 169-178, (2009)
• [21] Fister I.. Fister I.. Yang X. S.. and Brest J.. "A comprehensive review of firefly algorithms". Swarm and Evolutionary Computation. . 13. 34-46, (2013).
• [22] Gandomi A. H.. Yang X. S.. and Alavi A. H.. "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems". Engineering with Computers. 29: 17-35, (2013)
• [23] Dorigo M. and Gambardella L. M.. "Ant colonies for the travelling salesman problem”. Biosystems. 43: 73-81, (1997)
• [24] Tuba M. and Jovanovic R.. "Improved ACO Algorithm with Pheromone Correction Strategy for the Traveling Salesman Problem”. International Journal of Computers Communications & Control. 8: 477-485, (2013)
• [25] Jovanovic R. and Tuba M.. "An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem”. Applied Soft Computing. 11: 5360-5366, (2011)
• [26] Jovanovic R. and Tuba M.. "Ant Colony Optimization Algorithm with Pheromone Correction Strategy for the Minimum Connected Dominating Set Problem”. Computer Science and Information Systems. 10. 133-149, (2013)
• [27] Bacanin N. and Tuba M.. "Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators”. Studies in Informatics and Control. 21: 137-146, (2012)
• [28] Tuba M. and Bacanin N.. "Artificial Bee Colony Algorithm Hybridized with Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Selection Problem”. Applied Mathematics & Information Sciences. 8: 2831-2844, (2014)
• [29] Brajevic I. and Tuba M.. "An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems”. Journal of Intelligent Manufacturing. 24: 729-740, (2013)
• [30] Subotic M. and Tuba M.. "Parallelized Multiple Swarm Artificial Bee Colony Algorithm (MS-ABC) for Global Optimization”. Studies in Informatics and Control. 23: 117-126, (2014)
• [31] Yang X. S.. "A New Metaheuristic Bat-Inspired Algorithm”. Nicso 2010: Nature Inspired Cooperative Strategies for Optimization. 284: 65-74, (2010)
• [32] Kiran M. S.. "TSA: Tree-seed algorithm for continuous optimization”. Expert Systems with Applications. 42: 6686-6698, (2015)
• [33] Tuba M.. Brajevic I.. and Jovanovic R.. "Hybrid Seeker Optimization Algorithm for Global Optimization”. Applied Mathematics & Information Sciences. 7: 867-875, (2013)
• [34] Tuba M. and Bacanin N.. "Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems”. Neurocomputing. 143: 197-207, (2014)
• [35] Dai C. H.. Chen W. R.. Song Y. H.. andZhu Y. F.. "Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization”. Journal of Systems Engineering and Electronics. 21: 300-311, (2010)
• [36] Mirjalili S.. Mirjalili S. M.. and Lewis A.. "Grey Wolf Optimizer”. Advances in Engineering Software. 69: 46-61, (2014)
• [37] Jayakumar N.. Subramanian S.. Ganesan S.. andElanchezhian E. B.. "Grey wolf optimization for combined heat and power dispatch with cogeneration systems”. International Journal of Electrical Power & Energy Systems. 74: 252-264, (2016)
• [38] Otsu N.. " A threshold selection method from gray-level histograms”. IEEE Transactions on Systems Man and Cybernetics. 9: 62-66, (1979)