# An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields  
[PERSON]  
1 Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China; [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (X.W.) [PERSON]  
1 Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China; [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (X.W.) [PERSON]  
1 Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi'an 710071, China; [EMAIL_ADDRESS] (W.Z. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (X.W.) [PERSON]  
2 School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China  
during the image semantic segmentation. It is the first time image classification on a pixel-level has been realized, which is a significant improvement in accuracy. Subsequently, a large number of FCN-based methods [16; 17; 18; 19; 20] have been addressed to promoting the development of image semantic segmentation. As one of the most popular pixel-level classification methods, the DeepLab models proposed by [PERSON] et al. [21; 22; 23] make use of the fully connected CRF as a separated post-processing step in their pipeline to refine the segmentation result. The earliest version of DeepLab-v1 [21] overcomes the poor localization property of deep networks by combining the responses at the final FCN layer with a CRF for the first time. By using this model, all pixels, no matter how far apart they lie, are taken into account, rendering the system able to recover detailed structures in the segmentation that were lost due to the spatial invariance of the FCN. Later, [PERSON] et al. extended their previous work and developed the DeepLab-v2 [22] and DeepLab-v3 [23] with improved feature extractors, better object scale modeling, careful assimilation of contextual information, improved training procedures, and increasingly powerful hardware and software. Benefiting from the fine-grained localization accuracy of CRFs, the DeepLab models are remarkably successful in producing accurate semantic segmentation results. At the same time, the superpixel method, a well-known image pre-processing technique, has been rapidly developed in recent years. Existing superpixel segmentation methods can be classified into two major categories: graph-based methods [24; 25] and gradient-ascent-based methods [26; 27]. As one of the most widely used methods, SLIC adapts a k-means clustering approach to efficiently generate superpixels, which has been proved better than other superpixel methods in nearly every respect [27]. SLIC deserves our consideration for the application of image semantic segmentation due to its advantages, such as low complexity, compact superpixel size, and good boundary adherence. Although researchers have made some achievements, there is still much room for improvement in image semantic segmentation. We observe that the useful details such as the boundaries of images are often neglected because of the inherent spatial invariance of FCN. In this paper, an improved image semantic segmentation method is presented, which is based on superpixels and CRFs. Once the high-level abstract features of images are extracted, we can make use of the low-level cues, such as the boundary information and the relationship among pixels, to improve the segmentation accuracy. The improved method is briefly summarized as follows: First, we employ FCN to extract the pixel-level semantic features, while the SLIC algorithm is chosen to generate the superpixels. Then, the fusion of the two obtained pieces of information is implemented to get the boundary-optimized semantic segmentation results. Finally, the CRF is employed to optimize the results of semantic segmentation through its accurate boundary recovery ability. The improved method possesses not only excellent feature extraction capability but also good boundary adherence.