text: Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.
  @cite_19: 
  @cite_1: Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not yet widely used for texture problems. This paper investigates the usage of deep learning techniques, in particular Convolutional Neural Networks (CNN), for texture classification in two forest species datasets - one with macroscopic images and another with microscopic images. Given the higher resolution images of these problems, we present a method that is able to cope with the high-resolution texture images so as to achieve high accuracy and avoid the burden of training and defining an architecture with a large number of free parameters. On the first dataset, the proposed CNN-based method achieves 95.77 of accuracy, compared to state-of-the-art of 97.77 . On the dataset of microscopic images, it achieves 97.32 , beating the best published result of 93.2 .
  @cite_17: Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture at- tributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, D-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. D-CNN substantially improves the state-of-the-art in texture, mate- rial and scene recognition. Our approach achieves 82.3 accuracy on Flickr material dataset and 81.1 accuracy on MIT indoor scenes, providing absolute gains of more than 10 over existing approaches. D-CNN easily trans- fers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, D-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at lo- calizing stuff categories and obtains state-of-the-art re- sults on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.
summary: Basic CNN architectures have been applied to texture recognition such as @cite_19 , in which a simple four layers network was used in the early stage of deep learning to classify the Brodatz database. More recently, Hafemann @cite_1 applied CNN to a forest species classification, similar to a texture classification problem. While more complex and more accurate than @cite_19 , this approach still does not take the characteristics of texture images (statistical properties and repeated patterns) into consideration as it is a simple application of a standard CNN to a texture dataset. @cite_17 demonstrated the relevance of densely extracting texture descriptors from a CNN with the FV-CNN. They obtain impressive results on both texture recognition and texture recognition in clutter datasets. This approach is well suited to region recognition as it requires computing the convolution layer output once and pooling regions with FV separately. However, the CNN part of the FV-CNN does not learn from the texture dataset. A pre-trained network extracts the outputs of the convolution layers without being finetuned on the texture dataset. The fully connected layers are replaced with FV encoding and SVM classification, also making this approach computationally expensive.