X. Liu, Z. Deng, and Y. Yang. (2018)cite arxiv:1809.10198Comment: Pubulished at Artificial Intelligence review.
Semantic image segmentation, which becomes one of the key applications in
image processing and computer vision domain, has been used in multiple domains
such as medical area and intelligent transportation. Lots of benchmark datasets
are released for researchers to verify their algorithms. Semantic segmentation
has been studied for many years. Since the emergence of Deep Neural Network
(DNN), segmentation has made a tremendous progress. In this paper, we divide
semantic image segmentation methods into two categories: traditional and recent
DNN method. Firstly, we briefly summarize the traditional method as well as
datasets released for segmentation, then we comprehensively investigate recent
methods based on DNN which are described in the eight aspects: fully
convolutional network, upsample ways, FCN joint with CRF methods, dilated
convolution approaches, progresses in backbone network, pyramid methods,
Multi-level feature and multi-stage method, supervised, weakly-supervised and
unsupervised methods. Finally, a conclusion in this area is drawn.