# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 7 Experimental Evaluation
### Discussion  
The standard pipeline for semantic object segmentation with standard machine learning or deep neural networks consists of training, validation, and test parts. The training is usually performed on a large subset containing many annotated objects. The time spent for the annotation and training is rarely reported in the research, but it surely requires much time and energy. This experimental analysis investigated how accurate semantic association can be achieved without extensive training and annotation. The reported results proved that only a few annotated representative models were required to achieve the reported accuracy. In the case of simple objects, such as knives, caps, mugs, and laptops, only one or two annotated models were required to achieve an IoU of approximately 0.7. Figure 6: Component association accuracy depending on the number of representatives and the applied method measured by IoU. From the presented experimental analysis, it could be concluded that including neighborhood similarity in the SCLM improved the results significantly in the case of bags and lamps, which had more complex shapes than mugs, knives, caps, and laptops. In the case of mugs, knives, caps, and laptops, which had simple shapes, the information about the topological relations between components did not enhance the ability of the algorithm to identify the target component. For all classes except the lamps, the MST component association method would be the method of choice, because it provided a relatively high IoU for a small number of representatives, which did not grow significantly with the number of representatives. In the case of mugs, caps, and laptops, a single representative was sufficient to identify the target component in the remaining objects with IoU \(>\) 0.70. In the case of knives, the wrong selection of the first representative by the proposed method resulted in a very low IoU. However, with the second representative, an IoU close to 0.7 was reached. The MST method was expected to provide better results than the DSA in the case where the model database represented a sufficiently dense sampling of the considered object class. We considered a model database to be dense if for any two objects, a sequence of objects could be found where two consecutive objects had sufficiently similar shapes to allow unambiguous segment association. An example of such a sequence is shown in Figure 7. A bag handle of a reference object, shown in the top left image of Figure 7, was correctly associated with the segment represented by green points in the bottom right image of Figure 7. In an ideal case, the processing of such a model database by the MST method would allow correct component association using a single annotated representative object. However, in order for a dataset to be dense and cover large shape variations, it has to be very large. In order to evaluate how the size of the object dataset affected the accuracy of the MST-based component association, the procedure explained in Steps 1-12 was performed for all six classes with different numbers of models in the database, varying from 25% to 100% of the total No. of objects given in Table 2 and with the included neighborhood similarity, \(w_{N}=1\). The results are given in Table 5.  
Although the proposed method provided interesting results for a small numbers of representative objects, it did not achieve very high accuracy even when a high percentage of annotated representative objects was used. One of the reasons was that the proposed approach relied on a segmentation method \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Percent of Database/Class** & **Mug** & **Knife** & **Bag** & **Lamp** & **Cap** & **Laptop** \\ \hline
25\% & 0.721 & 0.755 & 0.400 & 0.080 & 0.706 & 0.762 \\
50\% & 0.747 & 0.726 & 0.419 & 0.177 & 0.732 & 0.694 \\
75\% & 0.752 & 0.749 & 0.604 & 0.400 & 0.793 & 0.739 \\
100\% & 0.763 & 0.732 & 0.609 & 0.530 & 0.759 & 0.761 \\ \hline \hline \end{tabular}
\end{table}