Digital image semantic segmentation algorithms: A survey

Wei Song, Nan Zheng, Rui Zheng, Xiao Bing Zhao, Antai Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In the field of computer vision, image semantic segmentation is an important research branch and it is also a challenging task. Applications such as autonomous driving, Unmanned Aerial Vehicle System (UAVS), and even virtual or augmented reality systems require accurate and efficient segmentation mechanisms. With the rise of deep learning methods, image semantic segmentation is more and more concerned by relevant researchers. In order to understand the research status, existing problems and development prospects of image semantic segmentation, this paper introduces the mainstream image semantic segmentation methods on the basis of extensive survey. First of all, we introduce the background concept of image semantic segmentation, generalize the commonly used image semantic segmentation methods, and compare the segmentation results of each method. After that, the commonly used image semantic segmentation datasets are summarized. At the same time, several commonly evaluation standards are introduced. Finally, the future development trend of image semantic segmentation is prospected, with a view to providing some ideas for researchers who wish to engage in this field.

Original languageEnglish (US)
Pages (from-to)196-211
Number of pages16
JournalJournal of Information Hiding and Multimedia Signal Processing
Volume10
Issue number1
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Keywords

  • Deep learning
  • Image semantic segmentation
  • Neural network

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