## 3 Experiments
### Texture Classification  
First of all, we are motivated by the problem of texture images' modeling and categorization. Unlike natural images, which include certain objects and structures, texture images are a special category of images and do not have a well defined shape. Texture characteristics play an important role in visual content analysis and are a well-studied property of images. It has been widely exploited before as an important indicator for the categorization and classification of images [36; 37; 38; 39]. A number of computer vision applications use the texture information such as information retrieval, material classification [40], object detection [37; 41], image segmentation [36; 42; 43] and facial expression recognition [38]. Previous texture extraction techniques have been mainly based on Gray Level Co-occurring Histograms [44], Local Binary Pattern (LBP) [45] and different filter banks [46]. An in-depth examination of these approaches is beyond the scope of our study. Instead, we are focusing on an efficient technique to extract relevant texture features and this technique has produced interesting results (DMD) [25]. It is based on the representation of texture using a set of local features called dense micro-block difference (DMD). The obtained features do not involve any quantization, thus retaining the complete information. In addition, these features do not involve any thresholding step and have the advantage of being quick and simple, like binary features. They are capable of achieving high discrimination and are considered invariant with respect to orientation, scale and resolution. After extracting these features, we encode them using a Fisher vector method [25] to obtain a set of descriptors of the underlying image. Thus, each image is represented by a robust multidimensional vector of descriptors that involve high-order statistics (the Matlab code for the features is available at [[http://www.cs.tut.fi/~mehta/texturedmd](http://www.cs.tut.fi/~mehta/texturedmd)]([http://www.cs.tut.fi/~mehta/texturedmd](http://www.cs.tut.fi/~mehta/texturedmd)) (accessed on 1 February 2021)). The last step is to classify the resulting descriptors using a linear support vector machine classifier, which results in the categorization of texture images into homogeneous categories. In all our experiments, we use a linear SVM classifier, which is able to directly operate on the vector of features and also requires less training time over other SVM kernels. The performance study is reported in terms of accuracy based on 10 fold cross validation. Experimental evaluation involves the following texture databases. The first dataset, named UIUCTex [47], contains 25 texture classes and each one includes 40 images, thus, each has 40,640 \(\times\) 480 pixel images. The textures of this dataset are taken under different scales and viewpoint changes, including non-rigid deformations. The second dataset, named KTH-TIPS [40], contains images of ten classes (materials) and each one includes 81 images (200 \(\times\) 200 pixel ) per class. Images are captured with different combinations such as nine different scales, viewed under three different poses and illumination directions. The third dataset is the UMD dataset [48], which is composed of 25 textures classes including 40 samples (with resolution of 1280 \(\times\) 900) per each texture-class (a total number of 25 \(\times\) 40 = 1000 uncalibrated images). Images are captured under different significant distances and viewing directions, and the illuminations are uncontrolled. Figure 3 shows random samples of textured images from different classes presenting in the three datasets. For all these databases, 50% of images are randomly chosen for training and 50% for testing. In order to quantify how well textures are classified, we evaluate the performance with an accuracy metric.