Learning deep representations for semantic image parsing: a comprehensive overview

doi:10.1007/s11704-018-7195-8

Front. Comput. Sci.

2018, Vol. 12

Issue (5) : 840-857 https://doi.org/10.1007/s11704-018-7195-8

RESEARCH ARTICLE

Learning deep representations for semantic image parsing: a comprehensive overview

Lili HUANG, Jiefeng PENG, Ruimao ZHANG, Guanbin LI, Liang LIN(

)

School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China

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Abstract

Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.

Keywords semantic image segmentation deep learning convolutional neural networks image parsing

Corresponding Author(s): Liang LIN

Online First Date: 04 September 2018 Issue Date: 21 September 2018

Cite this article:

Lili HUANG,Jiefeng PENG,Ruimao ZHANG, et al. Learning deep representations for semantic image parsing: a comprehensive overview[J]. Front. Comput. Sci., 2018, 12(5): 840-857.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-7195-8
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I5/840

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