## 4 Discussion  
The results provided by the U-Net algorithm provided interesting results in terms of the actual misclassification results against traditional algorithms, and the variability of the U-Net cases. The segmentation results provided by the U-Nets were better in four of the six images. In some cases, the results were very close to the second best option (a: 2.8/2.6, d: 7.3/7.1) and in two cases (e.f) traditional algorithms provided better results (e: 4.3/7.7, f: 17.0/17.5). The average for all the six composites was best for U-Nets, however, given the fact that the difference with the second best is relatively small (0.75), and that traditional algorithms provided better results in 1/3 cases shows that care should be taken when selecting algorithms. This is similar to the conclusion of [PERSON] who stated that \"No single approach did perform best or very close to the best for all images\" [55]. In terms of the U-Net configuration there are several interesting observations. First, there was a great variability in the results produced by the different U-Net configurations. It was surprising that the maximum value of the misclassification in some cases was extremely high, 80% in the cases of 5 textures and 94% in the case of 16 textures, those cases are equivalent of selecting a single class for all textures. Second, three of the best results were obtained with 100 epochs, 2 with 10 epochs, and 1 with 50, which is counter-intuitive as it would be expected that longer training times would provide better results. Third, three of the best results were provided by RMSprop optimisation, two by Adam and one by sgdm. Fourth, and perhaps the most surprising result was that the results provided by the two 20 layer configurations were very different. In a few cases the result were equal (e.g., image c, sgdm, 10 epochs; image b, Adam, 10 epochs) but in others the variation was huge (e.g., image b, Adam, 50 epochs). Figure 7: (**a–f**) Results of the segmentation with U-Nets for the six texture arrangments. The misclassification (%) is shown in each case. Pixels that are correctly classified appear in white. In terms of texture, it can be highlighted that not all textures are the same, the five textures of image (a) are far easier to distinguish and correctly segment than those of image (b) and image (f). The U-Net was capable of segmenting these textures with accuracy comparable or better than traditional techniques. As mentioned previously, the fact that the textures have been histogram equalised removes the discrimination of the regions by their average intensities. More complex architectures, e.g., Siamese Networks [57] could provide better results, but it is important to use a standard benchmark such as that provided by [PERSON] [55]. There are many other configuration parameters that could be varied; _learning rate, batch size, variations of the training data, different number of layers_, but for the purpose of this work, the results show first, the capability of deep learning architectures for segmentation of textured images and second, in some cases better results than traditional methodologies. However, the configuration of the network is not trivial and variations of some parameters can provide sub-optimal results. The experiments conducted in this work did not provide conclusive evidence for the selection of any of the parameters evaluated. Furthermore, training of the networks requires considerable resources. The training times for the images with 5 textures took around 5 hours and for the image with 16 textures around 96 hours on a Apple (Cuppertino, CA, USA) Mac Pro (Late 2013) with a 3.7 GHz Quad-Core and 32 GB Memory with Dual AMD FirePro D300 graphics processors. Therefore, it can be concluded that U-Net convolutional neural networks can be used for texture segmentation and provide results that are comparable or better than traditional texture algorithms. Furthermore, these results encourage the application of deep learning to other areas.