Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1743-1749.DOI: 10.11772/j.issn.1001-9081.2022060945
Special Issue: CCF第37届中国计算机应用大会 (CCF NCCA 2022)
• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles
Jun LIANG, Zehong HONG, Songsen YU()
Received:
2022-06-29
Revised:
2022-08-29
Accepted:
2022-09-01
Online:
2022-09-22
Published:
2023-06-10
Contact:
Songsen YU
About author:
LIANG Jun, born in 1983, Ph. D., lecturer. His research interests include graph theory, machine learning, algorithm design.Supported by:
通讯作者:
余松森
作者简介:
梁军(1983—),男,江西高安人,讲师,博士,CCF会员,主要研究方向:图论、机器学习、算法设计基金资助:
CLC Number:
Jun LIANG, Zehong HONG, Songsen YU. Image segmentation model based on improved particle swarm optimization algorithm and genetic mutation[J]. Journal of Computer Applications, 2023, 43(6): 1743-1749.
梁军, 洪泽泓, 余松森. 基于改进粒子群优化算法和遗传变异的图像分割模型[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1743-1749.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060945
子图序号 | k | CEFO[ | WOA-DE[ | PSOM-K | |||
---|---|---|---|---|---|---|---|
PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | ||
(e) | 4 | 18.023 9 | 0.768 0 | 25.6199 | 0.8446 | ||
6 | 20.347 9 | 0.828 4 | 28.6938 | 0.9116 | |||
8 | 22.166 1 | 0.862 8 | 30.4093 | 0.9417 | |||
10 | 24.013 8 | 0.893 1 | 31.1996 | 0.9559 | |||
(g) | 4 | 18.873 9 | 0.751 9 | 27.0714 | 0.8473 | ||
6 | 22.144 3 | 0.849 8 | 29.8353 | 0.9140 | |||
8 | 23.783 9 | 0.885 8 | 31.6266 | 0.9526 | |||
10 | 24.743 3 | 0.906 6 | 32.2731 | 0.9679 | |||
(h) | 4 | 18.949 5 | 0.790 9 | 28.4873 | 0.8518 | ||
6 | 21.410 9 | 0.847 2 | 30.7282 | 0.9030 | |||
8 | 23.067 7 | 0.866 2 | 32.2919 | 0.9355 | |||
10 | 25.435 7 | 0.888 3 | 33.2634 | 0.9512 | |||
(j) | 4 | 17.778 1 | 0.734 0 | 26.0690 | 0.8483 | ||
6 | 24.977 0 | 0.819 7 | 29.1247 | 0.9213 | |||
8 | 28.609 2 | 0.879 8 | 30.4283 | 0.9519 | |||
10 | 30.668 7 | 0.903 9 | 30.9348 | 0.9643 | |||
(k) | 4 | 20.824 7 | 0.718 2 | 25.6015 | 0.7545 | ||
6 | 23.605 9 | 0.761 9 | 29.4465 | 0.8473 | |||
8 | 26.013 2 | 0.801 7 | 31.8695 | 0.8776 | |||
10 | 29.018 4 | 0.832 9 | 33.3502 | 0.9081 | |||
(l) | 4 | 18.655 8 | 0.715 1 | 26.6189 | 0.8511 | ||
6 | 22.248 1 | 0.792 1 | 30.0938 | 0.9239 | |||
8 | 24.882 1 | 0.843 5 | 31.4581 | 0.9481 | |||
10 | 27.911 6 | 0.874 5 | 31.9919 | 0.9587 |
Tab. 1 Comparison of PSNR and FSIM of coarse-grained image segmentation
子图序号 | k | CEFO[ | WOA-DE[ | PSOM-K | |||
---|---|---|---|---|---|---|---|
PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | ||
(e) | 4 | 18.023 9 | 0.768 0 | 25.6199 | 0.8446 | ||
6 | 20.347 9 | 0.828 4 | 28.6938 | 0.9116 | |||
8 | 22.166 1 | 0.862 8 | 30.4093 | 0.9417 | |||
10 | 24.013 8 | 0.893 1 | 31.1996 | 0.9559 | |||
(g) | 4 | 18.873 9 | 0.751 9 | 27.0714 | 0.8473 | ||
6 | 22.144 3 | 0.849 8 | 29.8353 | 0.9140 | |||
8 | 23.783 9 | 0.885 8 | 31.6266 | 0.9526 | |||
10 | 24.743 3 | 0.906 6 | 32.2731 | 0.9679 | |||
(h) | 4 | 18.949 5 | 0.790 9 | 28.4873 | 0.8518 | ||
6 | 21.410 9 | 0.847 2 | 30.7282 | 0.9030 | |||
8 | 23.067 7 | 0.866 2 | 32.2919 | 0.9355 | |||
10 | 25.435 7 | 0.888 3 | 33.2634 | 0.9512 | |||
(j) | 4 | 17.778 1 | 0.734 0 | 26.0690 | 0.8483 | ||
6 | 24.977 0 | 0.819 7 | 29.1247 | 0.9213 | |||
8 | 28.609 2 | 0.879 8 | 30.4283 | 0.9519 | |||
10 | 30.668 7 | 0.903 9 | 30.9348 | 0.9643 | |||
(k) | 4 | 20.824 7 | 0.718 2 | 25.6015 | 0.7545 | ||
6 | 23.605 9 | 0.761 9 | 29.4465 | 0.8473 | |||
8 | 26.013 2 | 0.801 7 | 31.8695 | 0.8776 | |||
10 | 29.018 4 | 0.832 9 | 33.3502 | 0.9081 | |||
(l) | 4 | 18.655 8 | 0.715 1 | 26.6189 | 0.8511 | ||
6 | 22.248 1 | 0.792 1 | 30.0938 | 0.9239 | |||
8 | 24.882 1 | 0.843 5 | 31.4581 | 0.9481 | |||
10 | 27.911 6 | 0.874 5 | 31.9919 | 0.9587 |
子图序号 | k | FSIM | PSNR/dB | 子图序号 | k | FSIM | PSNR/dB | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
HWOA | PSOM-K | HWOA | PSOM-K | HWOA | PSOM-K | HWOA | PSOM-K | ||||
(q) | 40 | 0.995 8 | 0.9965 | 37.608 | 41.424 | (t) | 40 | 0.9972 | 0.996 0 | 38.739 | 43.117 |
50 | 0.997 2 | 0.9973 | 39.575 | 42.978 | 50 | 0.9980 | 0.997 2 | 40.576 | 44.045 | ||
60 | 0.998 0 | 0.9980 | 41.131 | 44.153 | 60 | 0.9986 | 0.997 9 | 42.073 | 45.181 | ||
70 | 0.9985 | 0.998 4 | 42.543 | 45.280 | 70 | 0.9989 | 0.998 4 | 43.366 | 46.861 | ||
(r) | 40 | 0.9959 | 0.995 4 | 37.215 | 42.269 | (p) | 40 | 0.9976 | 0.993 2 | 38.009 | 41.836 |
50 | 0.997 2 | 0.9972 | 39.419 | 44.772 | 50 | 0.9983 | 0.993 8 | 39.942 | 42.586 | ||
60 | 0.9980 | 0.997 6 | 40.995 | 45.454 | 60 | 0.9987 | 0.996 6 | 41.542 | 44.199 | ||
70 | 0.9984 | 0.998 3 | 42.244 | 46.823 | 70 | 0.9990 | 0.997 1 | 42.765 | 45.079 | ||
(n) | 40 | 0.996 2 | 0.9965 | 37.819 | 41.487 | (u) | 40 | 0.9976 | 0.995 8 | 38.230 | 41.132 |
50 | 0.997 4 | 0.9975 | 39.681 | 43.532 | 50 | 0.9983 | 0.997 4 | 40.107 | 43.346 | ||
60 | 0.9982 | 0.998 1 | 41.413 | 44.054 | 60 | 0.9988 | 0.997 7 | 41.701 | 43.925 | ||
70 | 0.9986 | 0.998 4 | 42.780 | 45.340 | 70 | 0.9991 | 0.998 2 | 43.049 | 45.366 | ||
(s) | 40 | 0.9980 | 0.996 9 | 38.825 | 42.092 | (m) | 40 | 0.9985 | 0.997 5 | 39.324 | 43.176 |
50 | 0.9986 | 0.997 7 | 40.606 | 43.824 | 50 | 0.9988 | 0.998 0 | 41.175 | 44.527 | ||
60 | 0.9989 | 0.998 4 | 42.023 | 45.126 | 60 | 0.9992 | 0.998 7 | 42.693 | 45.222 | ||
70 | 0.9992 | 0.998 6 | 43.307 | 45.843 | 70 | 0.9993 | 0.998 9 | 43.863 | 46.184 | ||
(o) | 40 | 0.9969 | 0.993 8 | 38.268 | 41.758 | ||||||
50 | 0.9980 | 0.996 2 | 40.288 | 43.448 | |||||||
60 | 0.9985 | 0.997 0 | 41.796 | 44.850 | |||||||
70 | 0.9989 | 0.997 8 | 43.249 | 45.793 |
Tab. 2 Comparison of FSIM and PSNR of fine-grained image segmentation
子图序号 | k | FSIM | PSNR/dB | 子图序号 | k | FSIM | PSNR/dB | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
HWOA | PSOM-K | HWOA | PSOM-K | HWOA | PSOM-K | HWOA | PSOM-K | ||||
(q) | 40 | 0.995 8 | 0.9965 | 37.608 | 41.424 | (t) | 40 | 0.9972 | 0.996 0 | 38.739 | 43.117 |
50 | 0.997 2 | 0.9973 | 39.575 | 42.978 | 50 | 0.9980 | 0.997 2 | 40.576 | 44.045 | ||
60 | 0.998 0 | 0.9980 | 41.131 | 44.153 | 60 | 0.9986 | 0.997 9 | 42.073 | 45.181 | ||
70 | 0.9985 | 0.998 4 | 42.543 | 45.280 | 70 | 0.9989 | 0.998 4 | 43.366 | 46.861 | ||
(r) | 40 | 0.9959 | 0.995 4 | 37.215 | 42.269 | (p) | 40 | 0.9976 | 0.993 2 | 38.009 | 41.836 |
50 | 0.997 2 | 0.9972 | 39.419 | 44.772 | 50 | 0.9983 | 0.993 8 | 39.942 | 42.586 | ||
60 | 0.9980 | 0.997 6 | 40.995 | 45.454 | 60 | 0.9987 | 0.996 6 | 41.542 | 44.199 | ||
70 | 0.9984 | 0.998 3 | 42.244 | 46.823 | 70 | 0.9990 | 0.997 1 | 42.765 | 45.079 | ||
(n) | 40 | 0.996 2 | 0.9965 | 37.819 | 41.487 | (u) | 40 | 0.9976 | 0.995 8 | 38.230 | 41.132 |
50 | 0.997 4 | 0.9975 | 39.681 | 43.532 | 50 | 0.9983 | 0.997 4 | 40.107 | 43.346 | ||
60 | 0.9982 | 0.998 1 | 41.413 | 44.054 | 60 | 0.9988 | 0.997 7 | 41.701 | 43.925 | ||
70 | 0.9986 | 0.998 4 | 42.780 | 45.340 | 70 | 0.9991 | 0.998 2 | 43.049 | 45.366 | ||
(s) | 40 | 0.9980 | 0.996 9 | 38.825 | 42.092 | (m) | 40 | 0.9985 | 0.997 5 | 39.324 | 43.176 |
50 | 0.9986 | 0.997 7 | 40.606 | 43.824 | 50 | 0.9988 | 0.998 0 | 41.175 | 44.527 | ||
60 | 0.9989 | 0.998 4 | 42.023 | 45.126 | 60 | 0.9992 | 0.998 7 | 42.693 | 45.222 | ||
70 | 0.9992 | 0.998 6 | 43.307 | 45.843 | 70 | 0.9993 | 0.998 9 | 43.863 | 46.184 | ||
(o) | 40 | 0.9969 | 0.993 8 | 38.268 | 41.758 | ||||||
50 | 0.9980 | 0.996 2 | 40.288 | 43.448 | |||||||
60 | 0.9985 | 0.997 0 | 41.796 | 44.850 | |||||||
70 | 0.9989 | 0.997 8 | 43.249 | 45.793 |
子图序号 | k | k-means | SPSO-K | PSON-K | PSOM-K(本文) | ||||
---|---|---|---|---|---|---|---|---|---|
FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | ||
(a) | 4 | 0.773 0 | 24.209 7 | 0.880 6 | 26.328 2 | 0.888 6 | 26.711 4 | 0.8902 | 26.7604 |
6 | 0.861 5 | 26.145 3 | 0.930 9 | 29.332 8 | 0.935 0 | 29.564 9 | 0.9373 | 29.7241 | |
8 | 0.900 9 | 27.254 6 | 0.952 5 | 31.014 7 | 0.949 8 | 31.057 1 | 0.9542 | 31.1525 | |
10 | 0.915 5 | 27.960 1 | 0.960 9 | 31.718 0 | 0.9633 | 31.9603 | 0.962 0 | 31.804 7 | |
(b) | 4 | 0.725 8 | 23.232 1 | 0.894 0 | 27.363 4 | 0.871 9 | 26.133 3 | 0.8944 | 27.4712 |
6 | 0.807 1 | 25.104 1 | 0.911 2 | 29.050 8 | 0.910 6 | 29.328 4 | 0.9283 | 29.6454 | |
8 | 0.834 7 | 26.706 6 | 0.936 5 | 30.385 3 | 0.9421 | 30.158 9 | 0.941 0 | 30.7965 | |
10 | 0.850 1 | 26.772 2 | 0.948 9 | 31.557 0 | 0.947 7 | 31.8928 | 0.9508 | 31.556 7 | |
(f) | 4 | 0.767 3 | 21.661 0 | 0.837 6 | 24.947 6 | 0.838 5 | 24.974 6 | 0.8391 | 25.0042 |
6 | 0.794 8 | 23.495 0 | 0.880 4 | 27.122 8 | 0.891 4 | 25.011 3 | 0.8936 | 27.7084 | |
8 | 0.826 6 | 24.909 3 | 0.930 2 | 29.623 6 | 0.9349 | 29.293 1 | 0.933 4 | 29.7399 | |
10 | 0.861 6 | 26.055 4 | 0.952 1 | 30.687 1 | 0.949 9 | 29.965 3 | 0.9526 | 30.7011 | |
(c) | 4 | 0.730 6 | 24.670 0 | 0.753 3 | 25.304 2 | 0.751 8 | 24.546 0 | 0.7537 | 25.3283 |
6 | 0.834 4 | 27.749 4 | 0.868 5 | 29.017 6 | 0.874 7 | 29.188 3 | 0.8772 | 29.4119 | |
8 | 0.892 0 | 29.803 5 | 0.924 7 | 31.550 9 | 0.922 9 | 31.591 5 | 0.9272 | 31.6619 | |
10 | 0.924 6 | 31.175 7 | 0.945 5 | 32.399 3 | 0.946 2 | 31.959 2 | 0.9464 | 32.5112 | |
(i) | 4 | 0.749 7 | 22.752 5 | 0.820 3 | 24.546 7 | 0.827 3 | 24.687 7 | 0.8331 | 24.9954 |
6 | 0.838 7 | 25.654 8 | 0.895 6 | 28.068 6 | 0.891 0 | 28.044 7 | 0.8956 | 28.0990 | |
8 | 0.863 6 | 26.467 8 | 0.920 5 | 29.640 3 | 0.9245 | 29.6940 | 0.921 3 | 29.597 0 | |
10 | 0.882 5 | 27.393 1 | 0.938 1 | 30.564 1 | 0.938 1 | 30.505 0 | 0.9383 | 30.5684 |
Tab. 3 FSIM and PSNR for different numbers of cluster centers
子图序号 | k | k-means | SPSO-K | PSON-K | PSOM-K(本文) | ||||
---|---|---|---|---|---|---|---|---|---|
FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | ||
(a) | 4 | 0.773 0 | 24.209 7 | 0.880 6 | 26.328 2 | 0.888 6 | 26.711 4 | 0.8902 | 26.7604 |
6 | 0.861 5 | 26.145 3 | 0.930 9 | 29.332 8 | 0.935 0 | 29.564 9 | 0.9373 | 29.7241 | |
8 | 0.900 9 | 27.254 6 | 0.952 5 | 31.014 7 | 0.949 8 | 31.057 1 | 0.9542 | 31.1525 | |
10 | 0.915 5 | 27.960 1 | 0.960 9 | 31.718 0 | 0.9633 | 31.9603 | 0.962 0 | 31.804 7 | |
(b) | 4 | 0.725 8 | 23.232 1 | 0.894 0 | 27.363 4 | 0.871 9 | 26.133 3 | 0.8944 | 27.4712 |
6 | 0.807 1 | 25.104 1 | 0.911 2 | 29.050 8 | 0.910 6 | 29.328 4 | 0.9283 | 29.6454 | |
8 | 0.834 7 | 26.706 6 | 0.936 5 | 30.385 3 | 0.9421 | 30.158 9 | 0.941 0 | 30.7965 | |
10 | 0.850 1 | 26.772 2 | 0.948 9 | 31.557 0 | 0.947 7 | 31.8928 | 0.9508 | 31.556 7 | |
(f) | 4 | 0.767 3 | 21.661 0 | 0.837 6 | 24.947 6 | 0.838 5 | 24.974 6 | 0.8391 | 25.0042 |
6 | 0.794 8 | 23.495 0 | 0.880 4 | 27.122 8 | 0.891 4 | 25.011 3 | 0.8936 | 27.7084 | |
8 | 0.826 6 | 24.909 3 | 0.930 2 | 29.623 6 | 0.9349 | 29.293 1 | 0.933 4 | 29.7399 | |
10 | 0.861 6 | 26.055 4 | 0.952 1 | 30.687 1 | 0.949 9 | 29.965 3 | 0.9526 | 30.7011 | |
(c) | 4 | 0.730 6 | 24.670 0 | 0.753 3 | 25.304 2 | 0.751 8 | 24.546 0 | 0.7537 | 25.3283 |
6 | 0.834 4 | 27.749 4 | 0.868 5 | 29.017 6 | 0.874 7 | 29.188 3 | 0.8772 | 29.4119 | |
8 | 0.892 0 | 29.803 5 | 0.924 7 | 31.550 9 | 0.922 9 | 31.591 5 | 0.9272 | 31.6619 | |
10 | 0.924 6 | 31.175 7 | 0.945 5 | 32.399 3 | 0.946 2 | 31.959 2 | 0.9464 | 32.5112 | |
(i) | 4 | 0.749 7 | 22.752 5 | 0.820 3 | 24.546 7 | 0.827 3 | 24.687 7 | 0.8331 | 24.9954 |
6 | 0.838 7 | 25.654 8 | 0.895 6 | 28.068 6 | 0.891 0 | 28.044 7 | 0.8956 | 28.0990 | |
8 | 0.863 6 | 26.467 8 | 0.920 5 | 29.640 3 | 0.9245 | 29.6940 | 0.921 3 | 29.597 0 | |
10 | 0.882 5 | 27.393 1 | 0.938 1 | 30.564 1 | 0.938 1 | 30.505 0 | 0.9383 | 30.5684 |
子图序号 | SPSO-K | PSON-K | PSOM-K(本文) | |||
---|---|---|---|---|---|---|
FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | |
(a) | 0.007 930 | 0.231 071 | 0.001 372 | 0.111 699 | 0.000 922 | 0.057 527 |
(b) | 0.016 982 | 0.661 067 | 0.006 352 | 0.185 885 | 0.005 082 | 0.161 099 |
(f) | 0.006 971 | 0.099 664 | 0.005 183 | 0.352 617 | 0.004 288 | 0.238 007 |
(c) | 0.004 831 | 0.227 720 | 0.006 952 | 0.284 249 | 0.003 617 | 0.184 261 |
(i) | 0.006 083 | 0.323 502 | 0.002 341 | 0.181 416 | 0.001 568 | 0.089 452 |
Tab. 4 Standard deviation comparison of SPSO-K, PSON-K and PSOM-K in FSIM and PSNR
子图序号 | SPSO-K | PSON-K | PSOM-K(本文) | |||
---|---|---|---|---|---|---|
FSIM | PSNR/dB | FSIM | PSNR/dB | FSIM | PSNR/dB | |
(a) | 0.007 930 | 0.231 071 | 0.001 372 | 0.111 699 | 0.000 922 | 0.057 527 |
(b) | 0.016 982 | 0.661 067 | 0.006 352 | 0.185 885 | 0.005 082 | 0.161 099 |
(f) | 0.006 971 | 0.099 664 | 0.005 183 | 0.352 617 | 0.004 288 | 0.238 007 |
(c) | 0.004 831 | 0.227 720 | 0.006 952 | 0.284 249 | 0.003 617 | 0.184 261 |
(i) | 0.006 083 | 0.323 502 | 0.002 341 | 0.181 416 | 0.001 568 | 0.089 452 |
1 | KANG W X, YANG Q Q, LIANG R P. The comparative research on image segmentation algorithms[C]// Proceedings of the 1st International Workshop on Education Technology and Computer Science - Volume 2. Piscataway: IEEE, 2009: 703-707. 10.1109/etcs.2009.417 |
2 | WANG Z Z. Robust segmentation of the colour image by fusing the SDD clustering results from different colour spaces[J]. IET Image Processing, 2020, 14(13): 3273-3281. 10.1049/iet-ipr.2019.1481 |
3 | SHIRLY S, RAMESH K. Review on 2D and 3D MRI image segmentation techniques[J]. Current Medical Imaging, 2019, 15(2): 150-160. 10.2174/1573405613666171123160609 |
4 | 杨雪丹,刘文萍. 基于阈值及像素聚类的分割算法性能比较[J]. 计算机工程与应用, 2014, 50(2):183-188, 193. 10.3778/j.issn.1002-8331.1204-0231 |
YANG X D, LIU W P. Comparison of algorithms and performance of thresholding and clustering segmentation[J]. Computer Engineering and Applications, 2014, 50(2): 183-188, 193. 10.3778/j.issn.1002-8331.1204-0231 | |
5 | COMANICIU D, MEER P. Mean shift: a robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619. 10.1109/34.1000236 |
6 | COMANICIU D, MEER P. Mean shift analysis and applications[C]// Proceedings of the 7th IEEE International Conference on Computer Vision - Volume 2. Piscataway: IEEE, 1999: 1197-1203. 10.1109/iccv.1999.790416 |
7 | COMANICIU D. An algorithm for data-driven bandwidth selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2):281-288. 10.1109/tpami.2003.1177159 |
8 | SHEIKN Y A, KHAN E A, KANADE T. Mode-seeking by medoidshifts[C]// Proceedings of the IEEE 11th International Conference on Computer Vision. Piscataway: IEEE, 2007: 1-8. 10.1109/iccv.2007.4408978 |
9 | VEDALDI A, SOATTO S. Quick shift and kernel methods for mode seeking[C]// Proceedings of the 2008 European Conference on Computer Vision, LNCS 5305. Berlin: Springer, 2008: 705-718. |
10 | 张琦,郑伯川,张征,等. 基于随机分块的稀疏子空间聚类方法[J]. 计算机应用, 2022, 42(4):1148-1154. |
ZHANG Q, ZHENG B C, ZHANG Z, et al. Sparse subspace clustering method based on random blocking[J]. Journal of Computer Applications, 2022, 42(4):1148-1154. | |
11 | CHAN C C H, HWANG Y R, WU H C. Marketing segmentation using the particle swarm optimization algorithm: a case study [J]. Journal of Ambient Intelligence and Humanized Computing, 2016, 7(6): 855-863. 10.1007/s12652-016-0389-9 |
12 | WANG Y, ZHANG Z X, CHI L J. User account risk identification model for web applications[C]// Proceedings of the 5th International Conference on Computer and Technology Applications. New York: ACM, 2019: 30-34. 10.1145/3323933.3324058 |
13 | HE S, LUO D, GUO K. Evaluation of mineral resources carrying capacity based on the particle swarm optimization clustering algorithm [J]. Journal of the Southern African Institute of Mining and Metallurgy, 2020, 120(12): 681-691. 10.17159/2411-9717/1139/2020 |
14 | IBRAHIM C, MOUGHARBEL I, KANAAN H Y, et al. Two stages K-means and PSO-based method for optimal allocation of multiple parallel DRPs application & deployment [J]. IET Smart Grid, 2020, 3(2): 216-225. 10.1049/iet-stg.2019.0019 |
15 | TAO X M, GUO W J, LI X K, et al. Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy [J]. Expert Systems with Applications, 2022, 191: No.116301. 10.1016/j.eswa.2021.116301 |
16 | ZHANG H X, PENG Q X. PSO and K-means-based semantic segmentation toward agricultural products [J]. Future Generation Computer Systems, 2022, 126:82-87. 10.1016/j.future.2021.06.059 |
17 | 高兵,郑雅,秦静,等.基于麻雀搜索算法和改进粒子群优化算法的网络入侵检测算法[J]. 计算机应用, 2022, 42(4): 1201-1206. |
GAO B, ZHENG Y, QIN J, et al. Network intrusion detection algorithm based on sparrow search algorithm and improved particle swarm optimization algorithm[J]. Journal of Computer Applications, 2022, 42(4):1201-1206. | |
18 | ZHANG L, ZHANG L, MOU X Q, et al. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8):2378-2386. 10.1109/tip.2011.2109730 |
19 | MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]// Proceedings of the 8th IEEE International Conference on Computer Vision. Piscataway: IEEE, 2001, 2: 416-423. |
20 | SONG S H, JIA H M, MA J. A Chaotic Electromagnetic field optimization algorithm based on fuzzy entropy for multilevel thresholding color image segmentation[J]. Entropy, 2019, 21(4): No.398. 10.3390/e21040398 |
21 | LANG C B, JIA H M. Kapur’s entropy for color image segmentation based on a hybrid whale optimization algorithm [J]. Entropy, 2019, 21(3): No.318. 10.3390/e21030318 |
22 | ABDEL-BASSET M, MOHAMED R, AbdelAZIZ N M, et al. HWOA: a hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation [J]. Expert Systems with Applications, 2022, 190: No.116145. 10.1016/j.eswa.2021.116145 |
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