Table 3. The performance of different features.
Methods | Precision | Recall | F1 |
---|---|---|---|
GF + Bayesian | 79.39% | 61.83% | 0.6952±0.14 |
GF + Decision Tree | 77.17% | 67.32% | 0.7191±0.07 |
GF + SVM | 77.42% | 81.31% | 0.7931±0.03 |
GF + Random Forest | 77.06% | 89.61% | 0.8286±0.06 |
TF + Bayesian | 94.16% | 66.95% | 0.7826±0.14 |
TF + Decision Tree | 84.63% | 86.47% | 0.8554±0.02 |
TF + Random Forest | 88.51% | 95.79% | 0.9200±0.00 |
TF + SVM | 96.11% | 92.87% | 0.9446±0.01 |
FF + Bayesian | 99.95% | 62.32% | 0.7677±0.14 |
FF + Decision Tree | 99.79% | 81.07% | 0.8946±0.01 |
FF + Random Forest | 99.82% | 100.00% | 0.9991±0.02 |
FF + SVM | 99.94% | 100.00% | 0.9997±0.00 |
GF, geometric features; SVM, support vector machine; TF, texture features; FF; fusion features.