# Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks
## 3 Experimental Results
### Results  
The architecture of the proposed CSVM involves many parameters. To identify a suitable architecture, we propose to investigate three main parameters, which are: the number of layers of the network, number of feature maps generated by each convolution layer and the spatial sizes of the kernels. To compute the best parameter values, we use a cross-validation technique with a number of folds equal to 3. Due to the limited number of training samples and the large number of possible combinations, we set the maximum number of possible layers to 3, the maximum number of SVMs in each layer to 512 with step of \(2^{i}\) (\(i=0,\ldots,9\)) and maximum kernel size for each layer is fixed to 10% of the size of the current map (we consider the minimum between the height and the width as the considered size) with step of 2. The obtained best values of the parameters by cross-validation are listed in Table 1. This table indicates that only one layer is enough for the first dataset, whereas the two other datasets require two layers to get the best performances. Concerning the number of SVMs and the related spatial size, dataset 3 presents the simplest architecture with just one SVM at the first layer and two SVMs at the second one with spatial size of 3. It is worth recalling that in the experiments.  
In order to evaluate our method, we compare it with results obtained using three different pre-trained CNNs, which are ResNet [23], GoogLeNet [24] and VGG16 [33]. All the results in terms of accuracies are reported in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Layer 1**} & \multicolumn{2}{c}{**Layer 2**} \\ \cline{2-4}  & **Number of SVMs** & **Spatial Size** & **Number of SVMs** & **Spatial Size** \\ \hline Dataset 1 & 512 & 7 & / & / \\ Dataset 2 & 32 & 7 & 256 & 3 \\ Dataset 3 & 1 & 3 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best parameter values of the convolutional support vector machine (CSVM) for each dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a)**} \\ \hline
**Method** & **SEN (\%)** & **SPE (\%)** & **AVG (\%)** \\ \hline ResNet & 71.16 & 93.84 & 82.50 \\ GoogLeNet & 78.65 & 94.34 & 86.49 \\ VGG16 & 74.53 & 94.58 & 84.55 \\ Ours & 89.14 & 84.26 & 86.70 \\ \hline \hline \multicolumn{4}{c}{**(b)**} \\ \hline
**Methods** & **SEN (\%)** & **SPE (\%)** & **AVG (\%)** \\ \hline ResNet & 89.54 & 96.38 & 92.96 \\ GoogLeNet & 83.63 & 96.86 & 90.25 \\ VGG16 & 81.81 & 96.14 & 88.98 \\ Ours & 93.64 & 92.17 & 92.90 \\ \hline \hline \multicolumn{4}{c}{**(c)**} \\ \hline
**Methods** & **SEN (\%)** & **SPE (\%)** & **AVG (\%)** \\ \hline ResNet & 64.17 & 92.40 & 78.29 \\ GoogLeNet & 62.50 & 93.27 & 77.88 \\ VGG16 & 64.32 & 93.98 & 79.15 \\ Ours & 80.79 & 82.27 & 81.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification obtained by M-CSVM compared to CNNs for: (**a**) dataset 1, (**b**) dataset 2, and (**c**) dataset 3.  
From these tables, it can be seen that in eight cases out of nine, our proposed method by far outperforms the different pre-trained CNNs. In seven cases the improvement is clearly important (more than 2%). Only in one case (ResNet, dataset 2) does a CNN method give a slightly better result than our method (92.96% compared to 92.90%).