# Semantic Segmentation Based on Depth Background Blur
###### Abstract  
Deep convolutional neural networks (CNNs) are effective in image classification, and are widely used in image segmentation tasks. Several neural netowrks have achieved high accuracy in segementation on existing semantic datasets, for instance PASCAL VOC, CamVid, and Cityscapes. However, there are nearly no studies on semantic segmentation from the perspective of a dataset itself. In this paper, we analyzed the characteristics of datasets, and proposed a novel experimental strategy based on bokeh to weaken the impact of futile background information. This crucial bokeh module processed each image in the inference phase by selecting an opportune fuzzy factor \(\sigma\), so that the attention of our network can focus on the categories of interest. Some networks based on fully convolutional networks (FCNs) were utilized to verify the effectiveness of our method. Extensive experiments demonstrate that our approach can generally improve the segmentation results on existing datasets, such as PASCAL VOC 2012 and CamVid.  
bokeh; fully convolutional networks; semantic segmentation +
Footnote †: journal: Medical  
of background information during the training phase not only increases the difficulty of learning, but also leads to misclassifications, see Figure 1. In view of the aforementioned issues, and motivated by the excellence of the self-attention mechanism in the segmentation task [17,18], we apply the attention mechanism to the dataset itself. Through in-depth analysis of the dataset, we propose the background blur module bokeh. The overall structure is shown in Figure 2, and a feasible strategy for selecting the fuzzy factor \(\sigma\) is proposed in Section 3.  
Humans, as the most sophisticated creatures on earth, have a natural advantage in patten recognition. Relying on foveated and active vision, our visual center always focusses on the area of interest to us, rather than on the background.  
Figure 1: An example in the PASCAL VOC 2012 dataset. (**a**) Chairs, sofas, and optical modems in the field of interest have the same color and shape as the walls and wardrobes in the background. (**b**) Areas of interest only occupy an unobtrusive part of the whole picture.  
Figure 2: (**a**) Existingmainstream semantic segmentation network architecture. (**b**) The segmentation network architecture of this paper. (**c**) bokeh module, see Section 3 for algorithm details.  
The proposed bokeh module mainly performs a certain degree of background blurring according to the distribution of various categories of the dataset itself, without any prior knowledge of the domain of interest. It combines the blurred background with the domain of interest as the subsequent segmentation network input. Specifically, during the training stage, background and foreground are divided accurately by real semantic labels provided by the training set. In the validation phase, the original segmentation network is able to separate the background and foreground, based on the coarse segmentation. The reconstructed segmentation network with the bokeh module performs the final semantic segmentation. The visualization of our bokeh module is shown in Figure 3.  
We demonstrate the effectiveness of our approach on two datasets, PASCAL VOC 2012 [19] and CamVid [20], and on several existing end-to-end network architectures.  
The main contributions of our paper are:  
1. Semantic segmentation is viewed from the dataset itself, and an inference strategy based on the background blurring module (bokeh) is easy to embed into existing semantic networks.
2. According to the characteristics of each dataset, an appropriate strategy for selecting the fuzzy factor \(\sigma\) is proposed.
3. It is verified from the FCN-based network that our bokeh module can improve the segmentation quality of the network without changing any network structure. The segmentation results of BiSeNet [14] on CamVid are improved by 3.7 points, while the performance of HyperSeg [21] on the PASCAL VOC 2012 is improved by 5.2 points after adding the bokeh module.