# Multiscale Analysis for Improving Texture Classification
## 1 Introduction  
The definition of texture has different flavors in the literature on computer vision. A common one considers texture as changes in the image intensity that form specific repetitive patterns [1]. These patterns may result from the physical properties of the object's surface (roughness) that provide different types of light reflection. A smooth surface reflects the light at a defined angle (specular reflection), while a rough surface reflects it in all directions (diffuse reflection). Although texture recognition is easy for human perception, automatic procedures are different, and this task sometimes requires complex computational techniques. In computer vision, texture analysis is of a notable role, and its basis is extracting relevant information from an image to characterize its texture. This process involves a set of algorithms and techniques. Since humans' perception of texture is not affected by rotation, translation, and scale changes, any numerical image texture characterization should be invariant to those aspects and any monotonic transformation in pixel intensity. Several image texture analysis approaches have been developed over the past few decades, researching various properties to characterize texture information within an image [2, 3]. For example, gray-level co-occurrence matrix (GLCM) [4], local binary patterns (LBP) [5], Haralick descriptors [6], Markov random fields [7], wavelet transform [8], Gabor texture discriminator among others, and more recently, the bioinspired texture descriptor (BiT) [9] are some of the classical and novel approaches developed for such a purpose. In addition, convolutional neural networks (CNNs) have attracted the interest of academics due to their performance in tasks such as object detection. However, they do not lend themselves particularly well to texture classification [10; 11]. [PERSON] and [PERSON] [10], for instance, introduced a basic texture CNN (T-CNN) architecture that pooled an energy measure at its final convolution layer and discarded the shape information often acquired by conventional CNNs. Although the results were positive, the trade-off between accuracy and complexity was not beneficial. Similarly, various CNN architectures for texture classification with good performance were reported in [11; 12; 13]. Texture descriptors are also widely used in medical and biological image analysis, particularly in histopathologic images (HIs), which contain a variety of textures. For instance, regions with a high/low nuclei concentration and stroma [14]. Several scholars have investigated many textural descriptors for HI classification, including GLCM, LBP, Gabor, and Haralick descriptors [15]. Even though the descriptors mentioned above have demonstrated significant discriminative strength alone, combining them may be a potential technique for providing a representation based on many intrinsic textural properties. To increase the performance of texture classification, we investigate the combination of texture descriptors such as BiT, information-theoretic measures, GLCM, and Haralick descriptors. For such an aim, the feature vector representing an image is constructed by concatenating the features provided by distinct descriptors. The rationale is to compensate for the potential loss associated with utilizing a single technique to describe the texture. This technique uses Gaussian-Laplacian pyramids (GLP), a helpful data structure for image analysis and processing, to represent a spectrum of spatial scales [16]. The central idea behind GLCM is to estimate a joint probability distribution \(P(x_{1},x_{2})\) for the grayscale values in an image, where \(x_{1}\) is the grayscale value at any randomly chosen pixel in the image and \(x_{2}\) is the grayscale value at another pixel that is at a specific distance \(d\) from the first pixel. In most cases, only a subset of the grayscale values in an image are used to build the GLCM matrix.