# Interactive Part Segmentation Using Edge Images
## 5 Results
### The IOU Accuracy for the Proposed Models and the State-of-the Art Interactive Segmentation Model  
The IOU accuracy of the target object of IOG [3] is higher than the accuracy of the proposed models. According to the IOG [3] paper, the IOU accuracy of object segmentation with the DeeplabV3+ [20] model trained with color images is about 90%. The IOU accuracy of object segmentation with proposed model 1 which is applied with DeeplabV3+ [20] is about 88%. The experimental results showed that the model trained with color images segmented objects more accurately than the proposed models trained with edge images. However, in the case of part segmentation, the accuracy of the proposed models trained with edge images was higher than that of IOG [3]. In particular, in the case of proposed model 1 using DeeplabV3+ [20], the mIOU exceeded 60%. Compared to the proposed models with edge images, the interactive segmentation model trained with color images has lower segmentation accuracy when texture information is not abundant or when the texture information when training and the input texture information are very different. Proposed model 2 using EfficientNetV2 [22] as an encoder and adding FineNet shows that the IOU accuracy increases when segmenting objects, but when segmenting parts, the accuracy of that is not higher than that of proposed model 1. Table 7 summarizes the results for each model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Input Image** & \begin{tabular}{c} **Object IOU** \\ **Accuracy** \\ \end{tabular} & \begin{tabular}{c} **Part IOU** \\ **Accuracy (mIOU)** \\ \end{tabular} \\ \hline Proposed model 1 & \begin{tabular}{c} Layer 1 edge \\ image \\ \end{tabular} & 73.8\% & 57.7\% \\ Proposed model 1 & \begin{tabular}{c} Layer 2 edge \\ image \\ \end{tabular} & 86.8\% & 58.8\% \\ Proposed model 1 & \
\begin{tabular}{c} Layer 3 edge \\ image \\ \end{tabular} & 88.1\% & 60.3\% \\ Proposed model 1 &
\begin{tabular}{c} Result edge \\ image \\ \end{tabular} & 87.9\% & 59.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The object and part IOU accuracy of the proposed model 1 for each edge image. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input Image** &
\begin{tabular}{c} **Object IOU** \\ **Accuracy** \\ \end{tabular} \\ \hline Proposed model 1 & Layer 1 edge image & 66.8\% \\ Proposed model 1 & Layer 2 edge image & 72.4\% \\ Proposed model 1 & Layer 3 edge image & 79.1\% \\ Proposed model 1 & Result edge image & 73.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The object IOU accuracy of the proposed model 1 for each edge image. The IOU Accuracy of Part Segmentation for the Proposed Interactive Segmentation Models and the State-of-the Art Semantic Part-Segmentation Model  
We compared the proposed models with the interactive object segmentation model and semantic part segmentation. Table 8 shows the accuracy of part segmentation for each object class in the PASCAL VOC Part dataset. Avg. is the average of each object class mIOU. The interactive object segmentation model, which can segment the area of the target object or the part with user interaction and the input image, has higher accuracy than semantic part-segmentation models, which can segment the area of the part and recognize the class of part using only input image. In the interactive object segmentation models, proposed model 1 (using DeeplabV3+ [20]), with the highest mIOU accuracy, also had the highest average (64.8%). Although proposed model 1 did not have higher IOU accuracy for all object classes than other interactive segmentation models, the number of higher IOU accuracies for each object class was greater than among the comparison models. Even for the object class that did not have the highest IOU accuracy with proposed model 1, the difference between the accuracy of the model with the highest IOU accuracy and the accuracy of proposed model 1 was less than 1%.