# Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
## 4 Experiments
### Experimental Results Analysis
#### 4.3.2 Ablation Experiments
This suggests that models without feature fusion struggle to effectively capture multi-scale features from target domain samples, leading to overall performance degradation;  
Rw \(\rightarrow\) Cl: the NFF's recall rate was 53.24%, markedly lower than the MMTADAN's 75.47%, further affirming the importance of the feature fusion module in cross-domain tasks;  
Cl \(\rightarrow\) Ar: Although the NFF performed slightly better in this task, with an accuracy of 76.87%, it still fell short of the MMTADAN's 86.24%. This indicates that the feature fusion module generally provides advantages in handling diverse image features. The contribution of the pseudo-label classifier is evident: the NLC's performance was significantly inferior to that of the MMTADAN, demonstrating that the pseudo-label classifier effectively enhances the model classification performance and generalization capability in scenarios with limited target domain data labels. The feature fusion module \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Task** & **Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Ar \(\rightarrow\) Pr & NLC & 75.43\% & 77.34\% & 66.23\% & 0.71 \\  & NFF & 67.54\% & 70.23\% & 55.32\% & 0.62 \\  & MMTADAN & 85.67\% & 88.14\% & 80.47\% & 0.84 \\ Pr \(\rightarrow\) Rw & NLC & 69.23\% & 72.34\% & 58.43\% & 0.65 \\  & NFF & 62.45\% & 64.78\% & 52.34\% & 0.58 \\  & MMTADAN & 83.42\% & 85.78\% & 78.64\% & 0.82 \\ Rw \(\rightarrow\) Cl & NLC & 72.47\% & 75.34\% & 65.21\% & 0.70 \\  & NFF & 65.14\% & 67.34\% & 53.24\% & 0.60 \\  & MMTADAN & 82.57\% & 85.12\% & 75.47\% & 0.80 \\ Cl \(\rightarrow\) Ar & NLC & 83.44\% & 60.12\% & 47.32\% & 0.54 \\  & NFF & 76.87\% & 78.65\% & 68.34\% & 0.73 \\  & MMTADAN & 86.24\% & 88.42\% & 80.32\% & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experimental results of various methods with the Office–Home dataset. also showed a notable performance drop in the NFF model compared to the complete MMTADAN, indicating that multi-scale feature fusion captures richer and more nuanced image features, thereby improving the classification performance. In addition, to demonstrate the performance of the T-NET, three objective metrics were used to evaluate each algorithm: the Structural Similarity Index (SSIM), the Signal-Preserving Ability (SPA), and the Edge-Preserving Ability (EPA). The formula for the SSIM is defined as follows: \(\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{y}+c_{2}\right)}{\left( \mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2} \right)}\), where \(\mu_{x}\) and \(\mu_{y}\) are the means of x and y, \(\sigma_{x}\) and \(\sigma_{y}\) are the variances of x and y, and \(\sigma_{xy}\) is the covariance between x and y. \(c_{1}\) and \(c_{2}\) are very small constants, both set to 0.001. Specifically, the SPA is defined as \(\sum_{i=1}^{k}\lvert f(x_{i})-f^{\prime}(x_{i})\rvert\), and the EPA is defined as \(\sum_{i=1}^{k}\lvert\
abla(x_{i})-\
abla f^{\prime}(x_{i})\rvert\). In these two formulas, \(k\) and \(k^{\prime}\) represent the lengths of the one-dimensional signals \(f(x)\) and \(\
abla f(x)\), respectively, while \(f^{\prime}(x)\) denotes the result of filtering the signal \(f(x)\), which is the output of various detail enhancement algorithms applied to the signal \(f(x)\). The comparison methods include GIF [45], WLS [46], GGF [47], SPGIF [48], ILS [49], RGIF [47], and ZF [50]. From Table 4, it can be seen that the T-NET performed excellently in the SSIM, SPA, and EPA tests, demonstrating its superior performance.