Table 2.

Comparison of local features and kernels on the DTD data

Local descr.	Kernel
	Linear	Hellinger	add-${\mathit{\chi }}^2$	exp-${\mathit{\chi }}^2$
MR8	20.8 $\pm$ 0.9	26.2 $\pm$ 0.8	29.7 $\pm$ 0.9	34.3 $\pm$ 1.1
LM	26.7 $\pm$ 0.9	34.8 $\pm$ 1.2	39.5 $\pm$ 1.4	44.0 $\pm$ 1.4
Patch${}_{3\times 3}$	15.9 $\pm$ 0.5	24.4 $\pm$ 0.7	27.8 $\pm$ 0.8	30.9 $\pm$ 0.7
Patch${}_{7\times 7}$	20.7 $\pm$ 0.8	30.6 $\pm$ 1.0	34.8 $\pm$ 1.0	37.9 $\pm$ 0.9
LBP${}^u$	8.5 $\pm$ 0.4	9.3 $\pm$ 0.5	12.5 $\pm$ 0.4	19.4 $\pm$ 0.7
LBP-VQ	26.2 $\pm$ 0.8	28.8 $\pm$ 0.9	32.7 $\pm$ 1.0	36.1 $\pm$ 1.3
SIFT	45.2 $\pm$ 1.0	49.1 $\pm$ 1.1	50.9 $\pm$ 1.0	52.3 $\pm$ 1.2
Conv VGG-M	55.9 $\pm$ 1.3	61.7 $\pm$ 0.9	61.9 $\pm$ 1.0	61.2 $\pm$ 1.0
Conv VGG-VD	64.1 $\pm$ 1.3	68.8 $\pm$ 1.3	69.0 $\pm$ 0.9	68.8 $\pm$ 0.9

The table reports classification accuracy, averaged over the predefined ten splits, provided with the dataset

We marked in bold the best performing descriptors, SIFT and convolutional features, which we will cover in the following experiments and discussions