# Complex Color Space Segmentation to Classify Objects in Urban Environments
## 3 Image Segmentation Approach
### Cnm Architecture  
Figure 8 shows network architecture based on VGG-16 [32] used in this study. This architecture has 16 layers to train about 138 million of parameters. The network consists of five blocks of convolutional layers. Each block consists of two or three convolutional layers followed by a groping layer. The number of filters increases by 2, from 64 to 512. The _Dropout_ layers are added between one block and the next to avoid over-adjustment [33]. Each _Dropout_ layer reduces the connection between one block and the next. The flat layer connects convolutional blocks with the fully connected layer. The fully connected layers have 4096 neurons, including \"bias\" and the activation function, a ReLU in this case. The last fully connected layer is the output from the network. The number of neurons on this layer is the same as the number of categories. The activation function associated with the last fully connected layer is the Softmax or normalized exponential function for a multi-class problem. Algorithm 1 calculates the segmentation of input images used to process the training and validation dataset. This process is illustrated in Figure 9. A binary mask per category, known as _class mask_, is generated for each image on the dataset. The _class mask_ is then used to crop \(p\) patches randomly sized \([lu\times lv]=[60\times 80]\) for each category. About 30,000 patches were generated for all the classes using the Cityscape database, with approximately 3000 images. Figure 8: Modified convolutional neural network VGG-16. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Class**} & \multirow{3}{*}{\begin{tabular}{c} **\(\mathbf{\chi_{g}}\)-Axis** \\ **Blocks** \\ \end{tabular} } & \multicolumn{6}{c}{\begin{tabular}{c} **X\({}_{\mathbf{\pi}}\)-Axis** \\ **\end{tabular} } \\ \cline{2-10}  & & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**8**} & \multicolumn{1}{c}{**16**} & \multicolumn{1}{c}{**32**} \\ \cline{3-10}  & & \multicolumn{1}{c}{**Pixel Ratio**} & \multicolumn{1}{c}{**Void Images**} & \multicolumn{1}{c}{**Pixel Ratio**} & \multicolumn{1}{c}{**Void Images**} & \multicolumn{1}{c}{**Pixel Ratio**} & \multicolumn{1}{c}{**Void Images**} \\ \hline \multirow{3}{*}{**Cityscape**} & \multirow{3}{*}{\begin{tabular}{c} 4 \\ 8 \\ \end{tabular} } & 0.1427 & 130 & 0.3275 & 73 & 0.4355 & 4 & 0.4390 & 4 \\ \cline{2-10}  & & 8 & 0.5105 & 45 & 0.4697 & 47 & 0.4903 & 4 & 0.4620 & 4 \\ \cline{2-10}  & & 16 & 0.6817 & 4 & 0.6406 & 4 & 0.5418 & 4 & 0.4652 & 4 \\ \cline{2-10}  & & 32 & 0.7239 & 4 & 0.6817 & 4 & 0.5853 & 4 & 0.4850 & 4 \\ \hline \multirow{3}{*}{**Cityscape**} & \multirow{3}{*}{\
\begin{tabular}{c} 4 \\ 8 \\ \end{tabular} } & 4 & 0.0885 & 139 & 0.2179 & 94 & 0.2982 & 38 & 0.3068 & 38 \\ \cline{2-10}  & & 8 & 0.4251 & 71 & 0.3635 & 73 & 0.3843 & 38 & 0.4253 & 38 \\ \cline{2-10}  & & 16 & 0.5761 & 38 & 0.5494 & 38 & 0.4741 & 38 & 0.4704 & 38 \\ \cline{2-10}  & & 32 & 0.6114 & 38 & 0.5885 & 38 & 0.5278 & 38 & 0.4439 & 38 \\ \hline \multirow{3}{*}{**Cityscape**} & \multirow{3}{*}{
\begin{tabular}{c} 4 \\ 8 \\ \end{tabular} } & 4 & 0.2277 & 128 & 0.4983 & 71 & 0.7376 & 2 & 0.6822 & 2 \\ \cline{2-10}  & & 8 & 0.6493 & 43 & 0.6222 & 45 & 0.7327 & 2 & 0.6284 & 2 \\ \cline{2-10}  & & 16 & 0.8533 & 2 & 0.8310 & 2 & 0.7321 & 2 & 0.5709 & 2 \\ \cline{2-10}  & & 32 & 0.8346 & 2 & 0.8063 & 2 & 0.7089 & 2 & 0.6295 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of segmented image categories for CityScape. All patches were resized to \([96\times 96]\), \([128\times 128]\) and \([224\times 224]\) for use on the CNN. Figure 10a,b show the accuracy and loss chart, respectively, for training of 100 epochs using an image size of \((224\times 224)\). The _SmallVGG_ network model was also used to optimize its resources and keep performance results optimal. This network model reduces the original architecture presented in this study [32].