## 1 Introduction  
Colour texture analysis and classification play a pivotal role in many computer-vision applications such as surface inspection, remote sensing, medical image analysis, object recognition, content-based image retrieval and many others. It is no surprise, then, that texture has been an area of intense research activity for at least forty years and that a huge variety of descriptors has been proposed in the literature (see [1; 2; 3] for comprehensive and up-to-date reviews).  
In recent years, the advent of convolutional neural networks has dramatically changed the outlook in many areas of computer vision and has led to astonishing improvements in tasks like object, scene and face recognition [4; 5; 6; 7; 8]. The structure of a CNN differs from that of traditional, hand-designed descriptors in that the former contains a large number of parameters, which are to be determined through suitable training procedures. The training process is usually carried out on huge datasets (containing millions of images), which enables the nets to \"learn\" very complex image-to-feature and/or image-to-class mappings. More importantly, there is evidence that such mappings are amenable to being transferred from one domain to another, making networks trained on certain classes of images usable in completely different contexts [5; 9]. The consequences of this are far-reaching: although datasets large enough to train a CNN entirely from scratch are rarely available in practical tasks, pre-trained networks can in principle be used as off-the-shelf feature extractors in a wide range of applications.  
Inevitably, the CNN paradigm is changing the approach to texture analysis as well. However, though there is general consensus that convolutional networks are superior to hand-designed descriptors in tasks such as object and scene recognition [4; 5; 6], this superiority is still not quite clear when it comes to dealing with _fine-grained_ images, i.e., textures. As we discuss in Section 2, some recent results seem to point in that direction, but it is precisely the aim of this work to investigate this matter further. To this end, we comparatively evaluated the performance of a large number of classic and more recent hand-designed, local image descriptors against a selection of off-the-shelf features from last-generation CNN. We assessed the performance under both ideal and realistic conditions, with special regard to different degrees of intra-class variability. As we detail in Section 2, intra-class variability can be the consequence of the intrinsic structure of the texture--which can be more or less stationary--and/or of variations in the imaging conditions (e.g., changes in illumination, rotation, scale and/or viewpoint).  
On the whole, image features from pre-trained networks outperformed hand-crafted descriptors, but with some interesting exceptions. In particular, hand-crafted methods were still better than CNN-based features at discriminating between very similar colour textures under invariable imaging conditions and proved slightly more robust to rotation; whereas the results were split in the presence of variations of scale.  
Networks were markedly superior in all the other cases--particularly with non-stationary colour textures--and also emerged as more robust to multiple and uncontrolled changes in the imaging conditions, which are harder to model and compensate for in a priori feature design.  
In the remainder of the paper, we first put the work in the context of the recent literature (Section 2), then describe the datasets and image descriptors used in our study (Sections 3-4). We detail the experimental set-up in Section 5, discuss the results in Section 6 and conclude the paper with some final considerations in Section 7.