## 2 Experiments
### Experimental Method  
#### 2.3.1 Color Feature Extraction of She Ethnic Groups in Different Regions  
In order to describe the clothing information of the She ethnic group in different regions with higher accuracy, this paper proposes a method that combines color histograms and color moment features based on HSV color space to extract the color features of the She ethnic group clothing in different regions. (1) Color Space Quantization  
The choice of color space directly affects the extraction of color features. Compared to the RGB and CMYK color space models, the HSV color space, which consists of three color channels: Hue, Saturation, and Value, is more consistent with human visual perception [28; 29; 30]. This separation through \(H\), \(S\), and \(V\) enables a better capturing of various colors in clothing, especially in the case of the vibrant and rich colors frequently used in ethnic clothing [31]. Therefore, before the extraction of color features, the images are converted from the RGB color space to the HSV color space using the conversion formulas shown in Equations (1)-(3):  
\[H=\left\{ \begin{array}{ll}0,&if\ \max(R,G,B)=\min(R,G,B)\\ 60^{\circ}\times\frac{G-B}{\max(R,G,B)-\min(R,G,B)},&if\ \max=R\ and\ G\geq B\\ 60^{\circ}\times\frac{G-B}{\max(R,G,B)-\min(R,G,B)}+360^{\circ},&if\ \max=R\ and\ G<B\\ 60^{\circ}\times\frac{B-R}{\max(R,G,B)-\min(R,G,B)}+120^{\circ},&if\ \max=G\\ 60^{\circ}\times\frac{R-G}{\max(R,G,B)-\min(R,G,B)}+240^{\circ},&if\ \max=B\\ \end{array} \right. \tag{1}\]  
\[S=\left\{ \begin{array}{ll}0,&if\ \max=0\\ 1-\frac{\min(R,G,B)}{\max(R,G,B)},&if\ \max\
eq 0\\ \end{array} \right. \tag{2}\]  
\[V=\frac{\max(R,G,B)}{255} \tag{3}\]  
where \(R\), \(G\), and \(B\) represent the red, green, and blue channel values, respectively, \"max\" and \"min\" represent the maximum and minimum values among \(R\), \(G\), and \(B\). \(H\) belongs to the range [0\({}^{\circ}\), 360\({}^{\circ}\)], while \(S\) and \(V\) belong to the range [0, 1]. (2) Color Histogram Features  
The distribution of pixels in the image's H, S, and V color channels can be described using a color histogram. In order to obtain effective features of the She ethnic clothing images, this study employs a non-uniform quantization method to process the H, S, and V channels, reducing the excessive dimensionality of the feature vector and improving the  
Figure 2: Samples for the experiment. (**a**) Jingning; (**b**) Fu’an; (**c**) Luoyuan; (**d**) Xiapu; (**e**) Fuding. efficiency of classifier construction and recognition accuracy. In the experiment, H, S, and V were non-uniformly quantized into 16, 4, and 4 channels, respectively [32]. The three color channels were converted into a one-dimensional color feature vector, as shown in Equation (4), to further reduce the feature dimensionality:  
\[L=16H+4S+V \tag{4}\]  
where \(L\in[0,1,\ldots,255]\) means that the quantized color space consists of 256 color feature values, and the color histogram feature vector is obtained by \(\{L_{0},L_{1},\ldots,L_{255}\}\). The extracted histogram features for different regions of She ethnic clothing are shown in Figure 3.  
Color moment features  
Color moments are introduced as an effective supplement to compensate for the problem of ignoring pixel position distribution in color histograms. Color information is mainly distributed in low-order moments [33]. Therefore, in the HSV color space model, the first-order moment \(\mu_{i}\), second-order moment \(\sigma_{i}\), and third-order moment \(\omega_{i}\) are extracted from the image. The calculation formulas are shown in Equations (5)-(7). Since each pixel has three color components in HSV (Hue, Saturation, and Value), there are nine feature vectors for color moments \(\{\mu_{H},\sigma_{H},\omega_{H},\mu_{S},\sigma_{S},\omega_{S},\mu_{V},\sigma_{ V},\omega_{V}\}\):  
\[\mu_{i}=\frac{1}{N}\sum_{j=1}^{N}P_{i,j} \tag{5}\]  
\[\sigma_{i}=\left[\frac{1}{N}\sum_{j=1}^{N}\left(P_{i,j}-\mu_{i}\right)^{2} \right]^{1/2} \tag{6}\]  
Figure 3: Color histograms.