## 7 Conclusions  
We have compared the effectiveness and computational workload of traditional, hand-crafted descriptors against off-the-shelf CNN-based features for colour texture classification under ideal and realistic conditions. On average, the experiments confirmed the superiority of deep networks, albeit with some interesting exceptions. Specifically, hand-crafted descriptors still proved better than CNN-based features when there was little intra-class variability or where this could be modelled explicitly (e.g., rotations). The reverse was true when there was significant intra-class variability--whether due to the intrinsic structure of the images and/or to changes in the imaging conditions--and in general in all the other cases. Of the three aggregation techniques used for extracting features via pre-trained CNN (i.e., FC, BoVW and VLAD), the first outperformed the other two in all the conditions considered. This finding is in agreement with the results recently published by [PERSON] et al. [17], but differs from those presented by [PERSON] [16] in which VLAD (and to some extent BoVW) performed either better or at least as well as FC. Note, however, that in our comparison, we kept the number of features approximately equal for the three methods, whereas in [16], VLAD's feature vectors were significantly longer than FC and BoVW. Furthermore, consider that in our experiments, the aggregation was performed over an _external_--dataset-independent--dictionary, whereas [16] used dataset-specific (_internal_) dictionaries. Incidentally, it is worth noting that the FC configuration is the only one that allows a genuine off-the-shelf reuse of the networks in a strict sense, only requiring a resizing of the input images to fit the input field of the net. Among the hand-crafted descriptors, colour LBP variants such as OCLBP, IOCLBP and LCVBP gave the best results under stable illumination, whereas dense SIFT proved the most effective method in the presence of illumination changes. Pure colour descriptors (i.e., full and marginal colour histograms) were the best methods to deal with variations in scale. On the other hand, the performance of CNN-based features was rather stable across all the datasets and experiments, with the three ResNet models emerging as the best descriptors in nearly all experimental conditions. As for the computational cost, the best CNN-based features were approximately as fast to compute as their hand-crafted counterparts (Table 17). The feature vectors, however, are at least twice as long (Tables 2 and 3), which implies higher computational demand in the classification step. Finally, an interesting and rather curious result is the high affinity that emerged between local binary patterns and the Outex database: in most of the experiments in which this dataset was involved, the best descriptor was always an LBP variant, a finding that did not go unnoticed by other authors either [16]. **Supplementary Materials:** The datasets' file names, class labels and links to the original images are available at the Supplementary material. Feature extraction and classification were based on CATAcOMB (Colour And Texture Analysis tOolbox for Matlab), which is provided in the CATAcOMB.zip file. An on-line version of the toolbox will also made available at [[https://bitbucket.org/biancovic/catacomb](https://bitbucket.org/biancovic/catacomb)]([https://bitbucket.org/biancovic/catacomb](https://bitbucket.org/biancovic/catacomb)) upon publication. **Author Contributions:** Conceptualization, R.-B.C., F.B., P.N. and F.S. ; methodology, R.-B.C., F.B., P.N. and F.S. ; software, R.-B.C., F.B. and P.N. ; validation, R.-B.C., F.B., P.N., F.D.M. and F.S. ; resources, F.B., F.D.M. and P.N. ; data curation, R.-B.C. and F.B. ; writing, original draft preparation R.-B.C. and F.B. ; writing, review and editing, R.-B.C., F.B., F.D.M., P.N. and F.S. ; funding acquisition, F.B. and F.D.M.