English

A Survey of Semantic Segmentation

Computer Vision and Pattern Recognition 2016-05-13 v2

Abstract

This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant papers are given. Recently published approaches with convolutional neural networks are mentioned and typical problematic situations for segmentation algorithms are examined. A taxonomy of segmentation algorithms is given.

Keywords

Cite

@article{arxiv.1602.06541,
  title  = {A Survey of Semantic Segmentation},
  author = {Martin Thoma},
  journal= {arXiv preprint arXiv:1602.06541},
  year   = {2016}
}

Comments

Fixed typo in accuracy metrics formula; added value range of accuracy metrics; consistent naming of variables

R2 v1 2026-06-22T12:54:34.657Z