## 3 Results and Discussion
### Comparative Analysis of She Ethnic Clothing Classification
#### 3.3.2 Comparative Analysis of Feature Fusion Methods  
To validate the effectiveness of the FPA color feature fusion method, this study explores the combination of color histograms and color moments by setting multiple sets of weights. The results are then compared with the results obtained from the FPA color feature fusion approach. The fused features from each set are subjected to kernel principal component analysis to construct the input set for the CNN classification model. The cumulative contribution rate of the features is approximately 95%, ensuring the objectivity and consistency of the experiments.  
The testing samples were evaluated in terms of training time and classification accuracy. The results are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color Feature** & **Method** & **Training Time/s** & **Classification Accuracy/\%** \\ \hline \multirow{3}{*}{Single-color feature} & \(t_{1}\) & 24.56 & 86.49 \\ \cline{2-4}  & \(t_{2}\) & 22.37 & 81.08 \\ \hline \multirow{6}{*}{Multi-color feature fusion} & \(t_{1}+t_{2}\) & 23.45 & 87.83 \\ \cline{2-4}  & \(0.9\times t_{1}+0.1\times t_{2}\) & 21.69 & 89.19 \\ \cline{2-4}  & \(0.8\times t_{1}+0.2\times t_{2}\) & 21.42 & 90.54 \\ \cline{2-4}  & \(0.7\times t_{1}+0.3\times t_{2}\) & 21.39 & 93.24 \\ \cline{2-4}  & \(0.6\times t_{1}+0.4\times t_{2}\) & 22.07 & 94.59 \\ \cline{2-4}  & \(0.5\times t_{1}+0.5\times t_{2}\) & 21.59 & 91.89 \\ \cline{2-4}  & \(0.4\times t_{1}+0.6\times t_{2}\) & 22.17 & 90.54 \\ \cline{2-4}  & \(0.3\times t_{1}+0.7\times t_{2}\) & 22.49 & 86.48 \\ \cline{2-4}  & \(0.2\times t_{1}+0.8\times t_{2}\) & 21.65 & 83.78 \\ \cline{2-4}  & \(0.1\times t_{1}+0.9\times t_{2}\) & 21.89 & 82.43 \\ \cline{2-4}  & Proposed method & 13.25 & 97.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification effects of different features.  
Figure 6: Optimization process and results. (**a**) Comparison of optimization processes for FPA, QFPA, PSO, BA, HS, and ABC algorithms; (**b**) FPA optimization results.  
According to Table 3, it can be observed that the classification accuracy of the color histogram feature is higher than that of the color moment feature when used individually. This indicates a positive correlation between the richness of information and classification accuracy. However, due to the high dimensionality of the color histogram feature, the computational complexity is high, resulting in a longer computation time, even after dimensionality reduction through the kernel principal component analysis.  
In comparison with using a single feature, the fusion of multiple color features, achieved by weighting the color histograms and color moment features, effectively improves the classification accuracy for She clothing. The experiments also demonstrate that increasing the weight of the color moment feature can enhance the classification accuracy. However, when t\({}_{2}\) reaches 0.5, the classification accuracy begins to gradually decrease. This is because, as the weight of the color histogram feature decreases, the feature information contained in the fusion of multiple color features is reduced, leading to a decrease in classification accuracy.  
In this study, the FPA was used to optimize the best weight fusion, with a focus on adopting an adaptive mechanism for optimizing the color feature weights. The experimental results show that when the weights of the color histograms and color moment features are 0.645 and 0.355, respectively, the FPA color feature fusion achieves the highest classification accuracy of 97.29%, with a training time of only 13.25 s. This is because FPA color feature fusion can extract data features and obtain optimal weights for feature fusion more efficiently. Therefore, the effectiveness of the proposed FPA color feature fusion method is demonstrated, and it is adopted as the input feature for the She ethnic clothing classifier in this study.