# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 4 Experimental Results
### Training and Testing of FCN Model  
The used dataset in our work is PASCAL VOC 2012 with a total of 17,125 images. All of the targets are divided into 20 categories, excluding the background labeled as category 0. At the same time, in order to facilitate the expression of image semantics, PASCAL VOC 2012 assigns specific color labels and numbers to each category so that different categories of targets can be distinguished by color or numbers in image semantic segmentation. Figure 5 shows 20 categories of instance images and corresponding color labels. The dataset will be used to train and test the FCN network, and the detailed processed is shown as follows:  
First, divide the PASCAL VOC 2012 dataset. The dataset involves 20 foreground object categories and one background category, as shown in Figure 5. The original dataset contains less than 5000 pixel-level labeled images. The extra annotations provided by [30] have 10,582 pixel-level labeled images. We take 9522 (90%) images as the training dataset, 530 (5%) images as the validation dataset, and the remaining 530 (5%) images as the test dataset.  
Secondly, reconstruct the VGG-16 network for FCN. By converting the fully connected layers in the VGG-16 network into the convolution layers, a FCN-VGG16 model is acquired. After that, the FCN model is initialized by using the full 16-layer version, which is trained by OverFeat [21].  
Thirdly, in order to have a better training result, we run the FCN for 50 epochs on training dataset. At the same time, we use pixel-level label to supervise the FCN-VGG16 model. The training model classification accuracy over the epoch of training is shown in Figure 6. The line that is marked by 'train' shows the relationship between the classification accuracy of training dataset with the epoch. The line marked by 'val' shows the relationship between the classification accuracy of the validation dataset with the epoch. Due to the randomness of the model parameter initialization, the classification accuracy of the validation dataset may be higher than that of the training dataset at initial epochs. Furthermore, the knowledge learning from the training dataset cannot suit the validation dataset well, so the curve 'val' becomes lower than the curve 'train' over epoch, leading to an intersection point around certain epochs.  
Finally, validation dataset has been used to evaluate the training performance. The semantic segmentation results are obtained from FCN-8s, FCN-16s, and FCN-32s, respectively. Some of the experimental results are shown in Figure 7. The successful training model is used for semantic segmentation in further process.  
Figure 5: PASCAL VOC 2012 dataset of 20 categories, numbers, and color labels.  
Figure 6: FCN-VGG16 model classification accuracy over epochs of training.  
Figure 7: Comparison of original images, semantic segmentation results and ground truth. (**a**) Original images; (**b**) FCN-32s semantic segmentation results; (**c**) FCN-16s semantic segmentation results; (**d**) FCN-8s semantic segmentation results; and, (**e**) the ground truth.