Page 51 - Fister jr., Iztok, and Andrej Brodnik (eds.). StuCoSReC. Proceedings of the 2015 2nd Student Computer Science Research Conference. Koper: University of Primorska Press, 2015
P. 51
le 2: Comparison of best mean objective values, with mean CPU times computed by DE, PSO, ABC, CS, and hjDE using
Otsu criterion.
Test image M Mean PSNR values Best PSNR values
rand/1 best/1 current to random rand/1 best/1 current to random
best/1 to best/1
best/1 to best/1
19.060400 19.313000
Baboon 5 18.1344 18.3655 18.3612 18.1425 19.055000 19.057400 22.890600 23.928900
24.365000 24.010100 26.259100 26.598400
8 21.0159 21.7262 22.1329 21.3687 27.102000 26.624900 28.932900 28.730200
29.052900 27.718700 32.102300 32.506900
10 22.4483 22.8946 23.3143 24.5666 30.405500 31.318300
17.808400 18.126300
12 28.6212 25.8498 23.5886 24.918 18.436100 17.807900 22.260300 21.598900
22.146300 22.228500 23.392400 24.931900
15 24.2914 28.7404 27.4966 28.8786 24.120400 23.964400 26.572900 26.420300
26.859200 28.144400 29.830600 29.663600
Lena 5 17.3731 17.7323 17.7754 17.4229 30.855000 28.871100
18.419600 18.447400
8 20.303 20.0005 19.9081 20.8732 18.612700 18.157400 24.487500 24.623600
24.656400 24.653500 26.139400 25.585600
10 21.2402 21.2492 22.5055 21.9101 26.449000 26.019600 26.770500 27.283500
26.941100 27.028100 30.381100 28.991100
12 25.3207 22.7806 22.7796 21.4321 28.997200 30.301300
21.167400 21.089100
15 23.8341 24.7654 27.3944 23.1946 21.508500 21.017600 24.264600 24.787800
24.397700 24.742200 26.260400 27.177800
Peppers 5 17.4975 18.1431 18.3017 17.846 27.353000 26.242400 28.746300 28.699800
28.725100 28.754200 31.646000 31.114900
8 23.442 23.2891 22.9984 24.0323 31.824400 31.917100
2 5
10 25.8584 24.9253 25.4991 25.5769 10 3
12 25.9307 25.9187 26.665 26.2836
15 27.4515 25.8253 26.0986 26.9089
Woman 5 21.5085 21.0176 21.0176 21.0176
8 23.1922 23.2705 23.2292 23.436
10 25.5869 24.1715 26.0503 25.8962
12 27.7128 28.7043 26.8407 26.298
15 26.8773 29.841 31.6153 27.5219
Best 528 5
7. REFERENCES
[1] Adis Alihodzic and Milan Tuba. Improved bat
algorithm applied to multilevel image thresholding. The
Scientific World Journal, 2014, 2014.
[2] Ashish Kumar Bhandari, Vineet Kumar Singh, Anil
Kumar, and Girish Kumar Singh. Cuckoo search
algorithm and wind driven optimization based study of
satellite image segmentation for multilevel thresholding
using kapur’s entropy. Expert Systems with
Applications, 41(7):3538–3560, 2014.
[3] Sathya P Duraisamy, Ramanujam Kayalvizhi, et al. A
new multilevel thresholding method using swarm
intelligence algorithm for image segmentation. Journal
of Intelligent Learning Systems and Applications,
2(03):126, 2010.
[4] Iztok Fister Jr., Xin-She Yang, Iztok Fister, Janez
Brest, and Dusan Fister. A brief review of
nature-inspired algorithms for optimization. CoRR,
abs/1307.4186, 2013.
[5] Nobuyuki Otsu. A threshold selection method from
gray-level histograms. Automatica, 11(285-296):23–27,
1975.
[6] Rainer Storn and Kenneth Price. Differential
evolution–a simple and efficient heuristic for global
optimization over continuous spaces. Journal of global
optimization, 11(4):341–359, 1997.
[7] Xin-She Yang and Xingshi He. Swarm intelligence and
evolutionary computation: Overview and analysis. In
Xin-She Yang, editor, Recent Advances in Swarm
Intelligence and Evolutionary Computation, volume 585
of Studies in Computational Intelligence, pages 1–23.
Springer International Publishing, 2015.
[8] Yudong Zhang and Lenan Wu. Optimal multi-level
thresholding based on maximum tsallis entropy via an
artificial bee colony approach. Entropy, 13(4):841–859,
2011.
StuCoSReC Proceedings of the 2015 2nd Student Computer Science Research Conference 51
Ljubljana, Slovenia, 6 October
Otsu criterion.
Test image M Mean PSNR values Best PSNR values
rand/1 best/1 current to random rand/1 best/1 current to random
best/1 to best/1
best/1 to best/1
19.060400 19.313000
Baboon 5 18.1344 18.3655 18.3612 18.1425 19.055000 19.057400 22.890600 23.928900
24.365000 24.010100 26.259100 26.598400
8 21.0159 21.7262 22.1329 21.3687 27.102000 26.624900 28.932900 28.730200
29.052900 27.718700 32.102300 32.506900
10 22.4483 22.8946 23.3143 24.5666 30.405500 31.318300
17.808400 18.126300
12 28.6212 25.8498 23.5886 24.918 18.436100 17.807900 22.260300 21.598900
22.146300 22.228500 23.392400 24.931900
15 24.2914 28.7404 27.4966 28.8786 24.120400 23.964400 26.572900 26.420300
26.859200 28.144400 29.830600 29.663600
Lena 5 17.3731 17.7323 17.7754 17.4229 30.855000 28.871100
18.419600 18.447400
8 20.303 20.0005 19.9081 20.8732 18.612700 18.157400 24.487500 24.623600
24.656400 24.653500 26.139400 25.585600
10 21.2402 21.2492 22.5055 21.9101 26.449000 26.019600 26.770500 27.283500
26.941100 27.028100 30.381100 28.991100
12 25.3207 22.7806 22.7796 21.4321 28.997200 30.301300
21.167400 21.089100
15 23.8341 24.7654 27.3944 23.1946 21.508500 21.017600 24.264600 24.787800
24.397700 24.742200 26.260400 27.177800
Peppers 5 17.4975 18.1431 18.3017 17.846 27.353000 26.242400 28.746300 28.699800
28.725100 28.754200 31.646000 31.114900
8 23.442 23.2891 22.9984 24.0323 31.824400 31.917100
2 5
10 25.8584 24.9253 25.4991 25.5769 10 3
12 25.9307 25.9187 26.665 26.2836
15 27.4515 25.8253 26.0986 26.9089
Woman 5 21.5085 21.0176 21.0176 21.0176
8 23.1922 23.2705 23.2292 23.436
10 25.5869 24.1715 26.0503 25.8962
12 27.7128 28.7043 26.8407 26.298
15 26.8773 29.841 31.6153 27.5219
Best 528 5
7. REFERENCES
[1] Adis Alihodzic and Milan Tuba. Improved bat
algorithm applied to multilevel image thresholding. The
Scientific World Journal, 2014, 2014.
[2] Ashish Kumar Bhandari, Vineet Kumar Singh, Anil
Kumar, and Girish Kumar Singh. Cuckoo search
algorithm and wind driven optimization based study of
satellite image segmentation for multilevel thresholding
using kapur’s entropy. Expert Systems with
Applications, 41(7):3538–3560, 2014.
[3] Sathya P Duraisamy, Ramanujam Kayalvizhi, et al. A
new multilevel thresholding method using swarm
intelligence algorithm for image segmentation. Journal
of Intelligent Learning Systems and Applications,
2(03):126, 2010.
[4] Iztok Fister Jr., Xin-She Yang, Iztok Fister, Janez
Brest, and Dusan Fister. A brief review of
nature-inspired algorithms for optimization. CoRR,
abs/1307.4186, 2013.
[5] Nobuyuki Otsu. A threshold selection method from
gray-level histograms. Automatica, 11(285-296):23–27,
1975.
[6] Rainer Storn and Kenneth Price. Differential
evolution–a simple and efficient heuristic for global
optimization over continuous spaces. Journal of global
optimization, 11(4):341–359, 1997.
[7] Xin-She Yang and Xingshi He. Swarm intelligence and
evolutionary computation: Overview and analysis. In
Xin-She Yang, editor, Recent Advances in Swarm
Intelligence and Evolutionary Computation, volume 585
of Studies in Computational Intelligence, pages 1–23.
Springer International Publishing, 2015.
[8] Yudong Zhang and Lenan Wu. Optimal multi-level
thresholding based on maximum tsallis entropy via an
artificial bee colony approach. Entropy, 13(4):841–859,
2011.
StuCoSReC Proceedings of the 2015 2nd Student Computer Science Research Conference 51
Ljubljana, Slovenia, 6 October
