# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Ablation Study
#### 4.4.3 Evaluation Metrics Experiments  
In this section, we conduct a case study to examine the reliability and interpretability of evaluation metrics. As shown in Figure 6, we analyze an image containing a single target class (dog). The experimental results indicate that although Seg-XRes-CAM achieves the highest Dice score (0.79), effectively preserving the overall contour of the target object, it also retains a substantial portion of the background. This phenomenon is reflected in its relatively high image retention rate. In contrast, both Seg-Grad-CAM and our proposed method focus primarily on the head region of the target object, aligning more closely with human visual perception. Although these methods yield a comparatively lower Dice score, their explanations are more precise and intuitively understandable. In particular, our approach demonstrates the highest interpretability by accurately localizing the key decision-influencing regions while maintaining the lowest image retention rate. Meanwhile, Seg-Grad-CAM retains some irrelevant background regions due to noise interference.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mean Dice\({}_{H}\)**} \\ \hline \multirow{2}{*}{**Label**} & **w/o** & **w** & \multirow{2}{*}{**Label**} & **w/o** & **w** \\  & **Correction** & **Correction** & & **Correction** & **Correction** \\ \hline Person & 0.73 & **0.81** & Bear & 0.38 & **0.58** \\ Car & 0.40 & **0.60** & Zebra & 0.44 & **0.84** \\ Motorcycle & 0.48 & **0.80** & Giraffe & 0.46 & **0.79** \\ Airplane & 0.40 & **0.66** & Umbrella & 0.45 & **0.69** \\ Bus & 0.40 & **0.69** & Bowl & 0.25 & **0.38** \\ Train & 0.55 & **0.70** & Pizza & 0.51 & **0.68** \\ Truck & 0.39 & **0.55** & Chair & 0.23 & **0.35** \\ Boat & 0.30 & **0.55** & Couch & 0.44 & **0.47** \\ Traffic Light & 0.33 & **0.54** & Bed & 0.48 & **0.55** \\ Fire Hydrant & 0.51 & **0.81** & Dining Table & **0.54** & 0.48 \\ Bench & 0.27 & **0.49** & Toilet & 0.59 & **0.78** \\ Bird & 0.39 & **0.55** & TV & 0.51 & **0.73** \\ Cat & 0.39 & **0.48** & Laptop & 0.47 & **0.67** \\ Dog & 0.49 & **0.66** & Oven & 0.43 & **0.56** \\ Horse & 0.41 & **0.70** & Sink & 0.27 & **0.54** \\ Sheep & 0.40 & **0.68** & Refrigerator & 0.48 & **0.61** \\ Cow & 0.51 & **0.58** & Clock & 0.32 & **0.66** \\ Elephant & 0.52 & **0.75** & Teddy Bear & 0.46 & **0.73** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results of sign correction. The bold font number indicate the highest value corresponding to each label.  
Figure 5: Visualization comparison of the sign correction strategy ablation study. The grayscale heatmaps in the second column illustrate the directional nature of the values. In these heatmaps, blue represents positive values, white represents values close to zero, and red represents negative values. The intensity of the color scale corresponds to the absolute magnitude of the values.  
To further quantify the quality of the XAI method, we introduce the Preserved Effectiveness metric and compute the value \(PE\) for each method: Eigen-CAM (0.23), Seg-Grad-CAM (2.09), Seg-XRes-CAM (0.99), and our proposed method (4.71). The results clearly demonstrate the superiority of our approach in terms of the quality of the explanation. The \(PE\) metric effectively captures the spatial precision of the explanations, revealing the critical regions that influence the model decisions. A higher PE value indicates that the method provides a more refined and focused explanation, avoiding the inclusion of extensive background areas or weakly relevant regions, thus more accurately reflecting the core basis of the model's decision-making process. This multidimensional evaluation framework mitigates the limitations of relying on a single metric, ensuring that the explanation results are both interpretable and reliable, ultimately enhancing their practical value.