Table 2.

Testing result (Dataset I) of the four classifiers trained with the low- and high-level features extracted by the three pre-trained deep CNNs (the maximum was highlighted in each row).

Score╲Model		AlexNet					Places365-AlexNet					GoogLeNet
		L1	L3	L5	L6	L7	L1	L3	L5	L6	L7	L1	L2	L3	L4	L5
ACC (%)	SVClnr	88.3	89.7	88.5	81.3	80.5	87.9	89.8	86.7	80.4	82.4	87.6	89.0	90.0	90.4	90.2
	SVCrbf	72.2	75.3	85.9	84.5	88.8	50.0	78.9	87.9	87.9	85.6	86.2	85.1	86.1	89.8	89.4
	RF	86.4	87.0	83.6	82.8	83.9	87.0	86.7	83.2	81.4	80.6	86.6	86.4	86.9	87.4	87.0
	KNN	87.0	87.1	86.8	85.8	85.8	86.4	87.0	84.6	83.0	82.0	86.7	86.3	88.0	89.8	89.1
AUC	SVClnr	0.953	0.963	0.954	0.895	0.887	0.953	0.961	0.944	0.889	0.909	0.949	0.956	0.964	0.967	0.963
	SVCrbf	0.868	0.902	0.930	0.920	0.952	0.500	0.915	0.945	0.948	0.935	0.940	0.931	0.939	0.961	0.959
	RF	0.933	0.933	0.910	0.901	0.909	0.935	0.932	0.904	0.892	0.889	0.935	0.931	0.936	0.939	0.938
	KNN	0.944	0.942	0.940	0.930	0.932	0.938	0.943	0.925	0.914	0.904	0.939	0.938	0.948	0.961	0.955
TPR (%)	SVClnr	88.7	90.4	87.6	80.8	79.9	88.3	90.1	87.1	80.6	82.4	87.4	89.4	90.4	90.9	90.2
	SVCrbf	51.5	57.1	90.7	90	88.6	44.4	64.4	88.6	88.6	86.5	86.5	85.1	86	90.2	89.4
	RF	85.6	84.6	81.1	79.4	81.1	85.6	84.5	80.4	78.6	77.1	85.6	84.2	85.3	86.4	85.2
	KNN	88.6	89.1	84.2	85.4	86.1	87.8	90.5	88	85.1	84	88	89.7	90.4	89.8	89.0
PPV (%)	SVClnr	87.9	89.1	89.1	81.5	80.8	87.5	89.5	86.3	80.2	82.3	87.7	88.6	89.6	89.9	90.1
	SVCrbf	87.7	89.6	82.7	81.0	88.9	49.9	90.6	87.3	87.3	84.9	85.9	85.0	86.1	89.4	89.3
	RF	86.9	88.8	85.3	85.1	85.8	88.0	88.3	85.1	83.2	82.8	87.3	88.0	88.0	88.1	88.3
	KNN	85.8	85.6	88.7	86.0	85.5	85.3	84.5	82.3	81.6	80.7	85.7	83.9	86.2	89.7	89.1
F1 (%)	SVClnr	88.3	89.8	88.4	81.2	80.4	88.0	89.8	86.8	80.4	82.4	87.5	89.0	90.0	90.4	90.2
	SVCrbf	64.9	69.8	86.5	85.3	88.8	66.6	75.4	88.0	88.0	85.7	86.3	85.0	86.1	89.8	89.4
	RF	86.3	86.6	83.2	82.2	83.5	86.8	86.4	82.7	80.9	79.9	86.5	86.1	86.6	87.3	86.8
	KNN	87.2	87.4	86.4	85.7	85.9	86.6	87.4	85.1	83.3	82.4	86.8	86.8	88.3	89.8	89.1