A Survey of Image Semantic Segmentation Based on Deep Network

LUO Hui-lan; ZHANG Yun

doi:10.3969/j.issn.0372-2112.2019.10.024

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A Survey of Image Semantic Segmentation Based on Deep Network

更新时间：2025-07-16

- A Survey of Image Semantic Segmentation Based on Deep Network
- Acta Electronica Sinica Vol. 47, Issue 10, Pages: 2211-2220(2019)
- 作者机构：
  
  江西理工大学信息工程学院,江西,赣州,341000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.10.024
  CLC： TP391
- Published Online：25 October 2019，
  
  Published：2019
- 稿件说明：
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LUO Hui-lan, ZHANG Yun. A Survey of Image Semantic Segmentation Based on Deep Network[J]. Acta Electronica Sinica, 2019, 47(10): 2211-2220.
DOI：

LUO Hui-lan, ZHANG Yun. A Survey of Image Semantic Segmentation Based on Deep Network[J]. Acta Electronica Sinica, 2019, 47(10): 2211-2220. DOI： 10.3969/j.issn.0372-2112.2019.10.024.

摘要

图像语义分割不仅预测一幅图像中的不同类别，同时还定位不同语义类别的位置，具有重要的研究意义和应用价值.本文阐述了图像语义分割最新的研究成果和方法，从三个角度综述了基于深度卷积神经网络的图像语义分割模型，分别是基于候选区域模型、基于全卷积网络模型和基于弱监督学习的语义分割模型，对这三类模型的方法和结构进行了详细的研究和分析.并在PASCAL VOC 2012数据集上对一些代表性的语义分割算法的性能进行了比较分析.

Abstract

Image semantic segmentation not only predicts different categories in an image

but also orientates different semantic categories locations

which has important research significance and application value. This paper expounds the latest research results and methods of image semantic segmentation

and from three perspectives: based candidate region models、based full convolutional network models and based weakly-supervised learning models. The image semantic segmentation model methods and structure based on deep convolutional neural network are deeply overviewed in this paper. This paper compares the performance of some representative semantic segmentation algorithms on the PASCAL VOC 2012 dataset.

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