## 2 Materials and Methods
### Features
#### 2.1.4 Local Texture Features  
In the realm of artistic creation, creators manipulate the texture of each area to generate diverse visual effects in the picture. Through the integration of the interrelationships among various components, a complete work is ultimately fashioned. For the extraction of local texture features, this study makes use of two extraction techniques: the GLCM and the LBP. * Gray Level Co-occurrence Matrix (GLCM);  
Through the analysis of the gray level relationship between pixel pairs (or pixel neighbor pairs) in the image, the texture information of the image is captured. This reflects the spatial distribution relationship between different gray levels in the image. Suppose the gray level range of the image is from 0 to L \(-\) 1; the GLCM is an L \(\times\) L matrix, where L represents the number of gray levels. The element \(GLCM(i,j)\) in the  
Figure 2: Kernels of different wavelengths \(\lambda\) and angles \(\theta\). GLCM indicates the frequency of occurrence of gray level \(i\) and gray level \(j\) in a specific spatial relationship within the image. For each pixel in a specific spatial relationship within the image. For each pixel \(I(x,y)\), the neighboring pixel \(I(x^{\prime},y^{\prime})\) at a distance d is examined. The frequency of occurrence of the pixel pair \((I(x,y),I(x^{\prime},y^{\prime}))\) is counted, and the count at the corresponding position in the GLCM is incremented by 1:  
\[ \begin{cases}1,\ \ if\ I(x,y)=i\ and\ I(x^{\prime},y^{\prime})=j\ at\ distance\ d\ and\ direction\ \theta\\ 0,\ \ otherwise\end{cases}  \tag{11}\]  
where \((x^{\prime},y^{\prime})\) denotes the coordinate of the neighboring pixel of the pixel \((x,y)\) at a distance d and in the direction \(\theta\). By quantifying the features of the GLCM, the texture of the image is reflected from multiple aspects. The degree of contrast between pixels with higher gray levels and pixels with lower gray levels in the GLCM. It reflects the contrast and roughness of the texture of the image. \[\ Contrast=\sum_{i=0}^{L-1}\sum_{j=0}^{L-1}(i-j)^{2}\text{-}GLCM(i,j)^{2} \tag{12}\]  
Energy: The square of the sum of the probabilities between pixel pairs in the GLCM, which reflects the uniformity of the image texture (refer to calculation Formula (5)). Entropy: The uncertainty of the probability distribution between pixel pairs in the GLCM, which reflects the complexity of the image texture. \[Entropy=-\sum_{i=0}^{L-1}\sum_{j=0}^{L-1}GLCM(i,j)log(GLCM(i,j)+\epsilon) \tag{13}\]  
* Local Binary Pattern (LBP);  
The fundamental principle of the LBP feature is to obtain a local binary coding for each pixel in the image by comparing its gray value with the neighboring pixels in the surrounding area. This is used to represent the texture feature of the pixel. For a specific pixel point \((x,y)\) in the image, a circular neighborhood with a radius of R is defined, which contains P sampling points and can be expressed as follows:  
\[(x_{i},y_{i})=\left(x+Rcos\left(\frac{2\pi i}{P}\right),y-Rsin\left(\frac{2\pi i }{P}\right)\right) \tag{14}\]  
where \(i=1,2,\dots,P-1\). For each pixel point \(x\) in the image, the LBP code \(LBP(x)\) is computed as follows:  
\[LBP(x)=\ \sum_{i=0}^{P-1}s(g(p_{i})-g(x))\times 2^{i} \tag{15}\]  
where \(g(p_{i})\) represents the gray value of the pixel point \(p_{i}\); \(g(x)\) represents the gray value of the central pixel \(x\), and \(s(v)\) is a step function that compares the gray value of each neighboring pixel with the gray value of the central pixel. If the gray value of the neighboring pixel is greater than or equal to the gray value of the central pixel, the pixel is marked as 1; otherwise, it is marked as 0:  
\[s(v)= \begin{cases}1,if\ v\geq 0\\ 0,\ if\ v<0\end{cases}  \tag{16}\]  
In this study, the 8-neighborhood LBP operator with \(R=1\) is utilized, so the LBP coding value range is 0-255.