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.