Table 6 Comparative metrics for each algorithm segmentation.
From: Optimized K-means algorithm for image segmentation based on improved dung beetle algorithm
imagery | algorithms | mean square error (\({\sigma _{MSE}}\) ) | peak signal-to-noise ratio (\({P_{PSNR}}\) ) |
|---|---|---|---|
Swan | DBO-K | 2.38E-01 | 46.19 |
K-means | 2.10E-01 | 44.91 | |
GWO-K | 1.32E-01 | 47.77 | |
BWO-K | 1.66E-01 | 45.95 | |
PSO-K | 9.00E-01 | 48.61 | |
MSDBO-K | 6.49E-01 | 46.37 | |
MODBO-K | 1.38E-01 | 46.52 | |
IDBO-K | 1.13E-01 | 48.88 | |
Cameraman | DBO-K | 2.39E-01 | 46.06 |
K-means | 3.70E-01 | 44.00 | |
GWO-K | 3.82E-01 | 44.04 | |
BWO-K | 3.40E-01 | 45.08 | |
PSO-K | 3.17E-01 | 45.96 | |
MSDBO-K | 2.32E-01 | 46.26 | |
MODBO-K | 2.78E-01 | 45.39 | |
IDBO-K | 1.96E-01 | 48.15 | |
Rice | DBO-K | 3.55E-01 | 41.51 |
K-means | 3.63E-01 | 41.33 | |
GWO-K | 4.17E-01 | 42.86 | |
BWO-K | 4.35E-01 | 42.75 | |
PSO-K | 5.13E-01 | 40.41 | |
MSDBO-K | 3.55E-01 | 41.78 | |
MODBO-K | 3.71E-01 | 41.57 | |
IDBO-K | 2.99E-01 | 43.18 | |
Tulip | DBO-K | 3.10E-01 | 42.63 |
K-means | 3.92E-01 | 42.25 | |
GWO-K | 3.72E-01 | 42.37 | |
BWO-K | 3.96E-01 | 42.18 | |
PSO-K | 3.17E-01 | 42.58 | |
MSDBO-K | 3.25E-01 | 42.49 | |
MODBO-K | 2.94E-01 | 43.07 | |
IDBO-K | 2.58E-01 | 43.63 |
