A Survey on Semantic Segmentation

摘要

Semantic Segmentation is a computer vision task for predicting the pixel labels corresponding to its belonging region or enclosing region area. It is an important part in many CV tasks and plays a significant role in machine learning. Semantic segmentation is aim at understanding special object class in the scene. In the paper, we will give a survey of Semantic Segmentation. At first, we make a brief introduction of Semantic Segmentation, introducing the wide use of semantic segmentation. Its range is from scene understanding, humanmachine interaction, computational photography, image search engine, predicting for the relationships of multiple objects mutual support in autonomous driving area. Next, we divide the Semantic Segmentation methods into two classes by the input modalities number. We make a big survey for different methods based on different structure, and show the reasons why these methods were introduced and how did they perform on the dataset. We figure out their contributions and significance. Then, we compare the basic dataset used in each method.