Review of Image Semantic Segmentation Based on Deep Learning

被引：0
|
作者：
Tian X. [1 ]
Wang L. [1 ]
Ding Q. [1 ]
机构：
[1] School of Information Science and Technology, Beijing Forestry University, Beijing
来源：
Ruan Jian Xue Bao/Journal of Software | 2019年 / 30卷 / 02期
关键词：
Deep learning; Fully supervised learning; Image semantic segmentation; Pixel classification; Weakly supervised learning;
D O I：
10.13328/j.cnki.jos.005659
中图分类号：
学科分类号：
摘要：
Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view: ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
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页码：440 / 468
页数：28
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