Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

Hongyuan Zhu, Fanman Meng, Jianfei Cai, Shijian Lu

Research output: Contribution to journalArticleResearchpeer-review

185 Citations (Scopus)


Image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation.

Original languageEnglish
Pages (from-to)12-27
Number of pages16
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Jan 2016
Externally publishedYes


  • Image cosegmentation
  • Image segmentation
  • Interactive image segmentation
  • Object proposal
  • Semantic image parsing
  • Superpixel
  • Unsupervised image segmentation
  • Weakly-supervised image segmentation

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