## 2 Experiments
### Experimental Method
#### 2.3.3 The Construction of the She Ethnic Clothing Classifier Based on the CNN  
Considering that the She ethnic clothing images consist of repetitive local information with low complexity, traditional approaches based on shape and highly complex features are not suitable. Instead, a more in-depth local feature extraction is required. The essence of CNN image classification lies in the convolutional layers that extract local features from images and pass them to fully connected layers to obtain global features of the clothing image, thereby achieving the fine-grained classification of the clothing images. Convolutional Neural Networks (CNNs) are a type of deep neural network with a convolutional operation, and they have a deep structure [38]. In this network, the convolutional structure is used to reduce the number of network parameters and the memory usage of deeper layers. The CNN mainly consists of the following two layers.  
1. Convolutional Layer  
The convolutional layer is a hidden layer that performs convolutional operations and serves as the fundamental structure of the CNN. The function of the convolutional layer is to connect each neuron in the current layer with the neurons in the previous layer within a convolutional window. It adopts parameter sharing to allocate the same set of parameters to neurons in the same layer [39]. The convolutional process can be expressed as follows in Equation (9):  
\[h_{ij}^{k}=f\left(\sum_{i=1}^{k}W^{k}*x_{ij}+b_{k}\right) \tag{9}\]  
where \(h_{i,j}^{k}\) represents the element at the \(k\)-th layer, the \(i\)-th row, and \(j\)-th column, \(W^{k}\) represents the value of the input data at position \((i,j),f\) denotes the activation function, and \(b_{k}\) represents the bias of the convolutional kernel. The convolutional layer has two convolutional kernels and two channels, with each channel corresponding to a convolutional kernel. The activation functions used in this study are ReLU and sigmoid functions.  
2. Pooling Layer  
The pooling layer exhibits the property of local connections. Its primary function is to perform down-sampling on the data, achieve feature selection, and further prevent overfitting [40]. The pooling layer differs from the convolutional layer in that it does not have weighted coefficients, and therefore it does not require training to optimize the computation results. The general expression of the commonly used pooling method, denoted as \(Lp\), is as follows:  
\[A_{k}^{I}(i,j)=\left[\sum_{x=1}^{f}\sum_{y=1}^{f}A_{k}^{I}(s_{0}i+x,s_{0}j+y)^ {p}\right]^{1/p} \tag{10}\]  
where \(s_{0}\) represents the parameters of the pooling layer and \(p\) is a predetermined parameter. When \(p\rightarrow\infty\), \(Lp\) is referred to as max pooling, which takes the maximum value within the pooling region. When \(p=1\), \(Lp\) calculates the average value within the pooling region and is known as average pooling.  
Figure 5 shows the CNN network structure, including the five-layer convolution layer and the three-layer maximum pooling layers. Each convolutional kernel has a size of \(3\times 3\). The level uses ReLU as the activation function to construct a maximum pooling layer to reduce the output dimension. The convolution step size of the max pooling layer is set to 2.