## 4 Methods
### Hand-Designed Descriptors
#### 4.1.2. Grey-Scale Texture Descriptors
For each resolution, we used non-interpolated neighbourhoods [75] composed of a central pixel and eight peripheral pixels, as shown in Figure 11.  
* Grey-Level Co-occurrence Matrices _(GLCM)_: Five global statistics (i.e., _contrast_, _correlation_, _energy_, _entropy_ and _homogeneity_) from grey-level co-occurrence matrices [76] computed using displacement vectors of lengths of 1 px, 2 px and 3 px and orientations of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 135\({}^{\circ}\) (5 \(\times\) 3 \(\times\) 4 = 60 features). A rotation-invariant version (GLCM\({}^{DFT}\)) based on discrete Fourier transform normalisation [77] was also considered. * Histograms of Oriented Gradients (HOG): Concatenation of three densely-computed 256-dimensional histograms of oriented gradients [78] (3 \(\times\) 256 = 768 features) estimated through Sobel filters of dimensions of 3 px \(\times\) 3 px, 5 px \(\times\) 5 px and 7 px \(\times\) 7 px. * Image Patch-Based Classifier, Joint version _(IPBC-J)_: Local image patches aggregated over a dictionary of visual words (Section 4.3) as proposed by [PERSON] and [PERSON] [79]. The image patches were captured at resolutions of 1 px, 2 px and 3 px using the same neighbourhood configuration shown in Figure 11. The resulting feature vectors were concatenated into a single vector. Further pre-processing involved zero-mean and unit-variance normalisation of the input image and contrast normalisation of each patch through Weber's law, as recommended in [79]. * Gabor features _(Gabor)_: Mean and standard deviation of the magnitude of Gabor-transformed images from a bank of filters with five frequencies and seven orientations. The other parameters of the filter bank were: frequency spacing half octave, spatial-frequency bandwidth one octave and aspect ratio 1.0. We considered both raw and contrast-normalised response: in the second case, the magnitudes for one point in all frequencies and rotations were normalized to sum to one. This option is indicated with subscript \"cn\"in the remainder. For both options, a DFT-based rotation-invariant version [80] (superscript \"DFT\" in the remainder) was also included in the experiments. In all the versions, the number of features was 2 \(\times\) 5 \(\times\) 7 = 70. * Wavelets _(WSF + WCF)_: Statistical (mean and standard deviation) and Co-occurrence features (same as in GLCM) from a three-level Wavelet decomposition as described in [81]. We used Haar's and bi-orthogonal wavelets, respectively indicated with subscript \"haar\" and \"bior22\" in the remainder. The number of statistical features was 2 \(\times\) 4 \(\times\) 3 = 24, and that of the co-occurrence features was 6 \(\times\) 4 \(\times\) 3 = 60, making a total of 84 features. Figure 11: Neighbourhoods used to compute features through histograms of equivalent patterns and other methods. * VZ classifier with MR8 filters _(VZ-MR8)_: Filter responses from a bank of 36 anisotropic filters (first- and second-derivative filters at six orientations and three scales) plus two rotationally-symmetric ones (a Gaussian and a Laplacian of Gaussian). Only eight responses are retained, i.e., the six maximum responses at each scale across all orientations and the response of the anisotropic filters [82]. The filter responses were aggregated over a dictionary of 4096 visual words (see Section 4.3). * Dense SIFT _(SIFT-B0 VW)_: Spatial histograms of local gradient orientations computed every two pixels and over a neighbourhood of radius 3 px (histograms of equivalent patterns). The resulting 128-dimensional local features were aggregated over a dictionary of 4096 visual words as described in Section 4.3. * Dense SIFT _(SIFT-VLAD)_: Same settings as SIFT-BoVW, but with Vectors of Locally-Aggregated Descriptors (VLAD) aggregation (Section 4.3) over 32 clusters.