Abstract
In this study, a new classification algorithm based on multilevel thresholding for color images has been proposed. Initially, thresholds for each channel of color images are determined by using histograms and bee algorithm. Then, the threshold values obtained are used for partition of RGB color space. Thus, pixels located in the relevant sub cubes were assigned in the same cluster and subsequently the results have been obtained.
In this study, a new classification algorithm based on multilevel thresholding for color images has been proposed. Initially, thresholds for each channel of color images are determined by using histograms and bee algorithm. Then, the threshold values obtained are used for partition of RGB color space. Thus, pixels located in the relevant sub cubes were assigned in the same cluster and subsequently the results have been obtained.
References
- Demirci, R.,Güvenç, U. ve Kahraman, H.T. Görüntülerin Renk Uzayı Yardımıyla Ayrıştırılması.İleri Teknoloji Bilimleri Dergisi, 3(1), 1-8, 2014.
- Sathya, P. D. and Kayalvizhi, R. Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement, 44(10), 1828-1848, 2011.
- D. Karaboga, An idea based on honey bee swarm for numerical optimization,Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
- Karaboğa D, “Yapay Zekâ Optimizasyon Algoritmaları,” Yapay Arı Kolonisi Algoritması, 4. Basım. Ankara, Türkiye: Nobel Yayın Dağıtım, 2011, böl. 9, ss. 207- 202.
- Kurugollu, F., Sankur, B. and Harmanci, A. E. (2001). Color image segmentation using histogram multithresholding and fusion. Image and vision computing, 19(13), 915-928, 2001.
- Rezaee, M. R., Lelieveldt, B. P. and Reiber, J. H, A new cluster validity index for the fuzzy c-mean. Pattern recognition letters, 19(3), 237-246,1998.[3] 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.
- MacQueen, J, Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability(Vol. 1, No. 14, pp. 281-297, 1967.
- Bezdek, J. C., Ehrlich, R. and Full, W,FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203, 1984.
- Otsu, N., A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66, 1979
- Kapur, J. N., Sahoo, P. K. and Wong, A. K,A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285, 1985
- Pal, S. S., Kumar, S., Kashyap, M., Choudhary, Y. And Bhattacharya, M.,Multi-level thresholding segmentation approach based on spider monkey optimization algorithm. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 273-287). Springer, New Delhi, 2016.
- Singh, R., Agarwal, P., Kashyap, M. and Bhattacharya, M,Kapur's and Otsu's based optimal multilevel image thresholding using social spider and firefly algorithm. In Communication and Signal Processing (ICCSP), 2016 International Conference on (pp. 2220-2224). IEEE.
- El Aziz, M. A., Ewees, A. A. and Hassanien, A. E.,Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83, 242-256, 2017.
Abstract
Bu çalışmada renkli
görüntüler için çok seviyeli eşikleme esaslı yeni sınıflandırma algoritması
önerilmiştir. Öncelikle renkli görüntülerin her bir kanalının histogramı ve arı
algoritması kullanılarak eşikler tespit edilmiştir. İkinci aşamada elde edilen
eşik değerleri RGB renk uzayının bölümlenmesinde kullanılmıştır. Böylece ortaya
çıkan alt küpler içerisinde kalan pikseller aynı sınıfa atanmış ve sonuçlar
alınmıştır.Ayrıştırma işleminin temel amacı, görüntü üzerinde farklı konumlardaki pikselleri gruplayarak anlamlı ve yararlı bilgi elde etmektir [2]. Söz konusu hedefe ulaşmak için günümüze kadar önerilen yöntemler ya kümeleme ya da eşikleme esaslı olmuştur. K-Ortalamalar[4] veya C-Ortalamalar[5] metotları görüntü ayrıştırmak için sıkça kullanılan iki algoritmadır. İlgili yaklaşımlar güçlü olmasına rağmen, hesaplama zamanları görüntü boyutuna bağlı olmakta ve gerçek zamanlı görüntü işlemede yetersiz kalmaktadırlar. Ayrıca, söz konusu yöntemlerin performansı küme merkezlerinin rastgele
References
- Demirci, R.,Güvenç, U. ve Kahraman, H.T. Görüntülerin Renk Uzayı Yardımıyla Ayrıştırılması.İleri Teknoloji Bilimleri Dergisi, 3(1), 1-8, 2014.
- Sathya, P. D. and Kayalvizhi, R. Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images. Measurement, 44(10), 1828-1848, 2011.
- D. Karaboga, An idea based on honey bee swarm for numerical optimization,Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
- Karaboğa D, “Yapay Zekâ Optimizasyon Algoritmaları,” Yapay Arı Kolonisi Algoritması, 4. Basım. Ankara, Türkiye: Nobel Yayın Dağıtım, 2011, böl. 9, ss. 207- 202.
- Kurugollu, F., Sankur, B. and Harmanci, A. E. (2001). Color image segmentation using histogram multithresholding and fusion. Image and vision computing, 19(13), 915-928, 2001.
- Rezaee, M. R., Lelieveldt, B. P. and Reiber, J. H, A new cluster validity index for the fuzzy c-mean. Pattern recognition letters, 19(3), 237-246,1998.[3] 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.
- MacQueen, J, Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability(Vol. 1, No. 14, pp. 281-297, 1967.
- Bezdek, J. C., Ehrlich, R. and Full, W,FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203, 1984.
- Otsu, N., A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66, 1979
- Kapur, J. N., Sahoo, P. K. and Wong, A. K,A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285, 1985
- Pal, S. S., Kumar, S., Kashyap, M., Choudhary, Y. And Bhattacharya, M.,Multi-level thresholding segmentation approach based on spider monkey optimization algorithm. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 273-287). Springer, New Delhi, 2016.
- Singh, R., Agarwal, P., Kashyap, M. and Bhattacharya, M,Kapur's and Otsu's based optimal multilevel image thresholding using social spider and firefly algorithm. In Communication and Signal Processing (ICCSP), 2016 International Conference on (pp. 2220-2224). IEEE.
- El Aziz, M. A., Ewees, A. A. and Hassanien, A. E.,Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Systems with Applications, 83, 242-256, 2017.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | August 1, 2018 |
| Published in Issue | Year 2018 Volume: 6 Issue: 4 |
