Image semantic segmentation using deep learning technique

Research Article
Open access

Image semantic segmentation using deep learning technique

Yifei Fan 1
  • 1 School of advanced technology, Xi’an Jiaotong-liverpool University Xi’an,111 Ren ai Road Suzhou Industrial Park Suzhou China.    
  • *corresponding author
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

With the deepening research on image understanding in many application fields, including auto drive system, unmanned aerial vehicle (UAV) landing point judgment, virtual reality wearable devices, etc., computer vision and machine learning researchers are paying more and more attention to image semantic segmentation (ISS). In this paper, according to the different region generation algorithms, the regional classification image semantic segmentation methods are classified into the candidate region method and the segmentation mask method, according to different learning methods, the image semantic segmentation methods based on super pixels are divided into fully supervised learning method and weakly supervised learning method. The typical algorithms in these various categories are summarized and compared. In addition, this paper also systematically expounds the role of DL technology in the field of ISS, and discusses the main challenges and future development prospects in this field.

Keywords:

Fan,Y. (2023). Image semantic segmentation using deep learning technique. Applied and Computational Engineering,4,810-817.
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References

[1]. Csurka G, Perronnin F. An efficient approach to semantic segmentation. 2011, Int. J. Com. Vis., 95(2): 198-212.

[2]. Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. 2006 Sci, 313(5786): 504-507.

[3]. Couprie C, Farabet C, Najman L, et al. Indoor semantic segmentation using depth information. 2013 arXiv preprint arXiv:1301.3572.

[4]. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014 Conf. Com. Vis. Pat. Rec. 580-587.

[5]. Girshick R. Fast r-cnn 2015 Conf. Com. Vis: 1440-1448.

[6]. Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. 2015 Adv. Neu. Inf. Pro. Sys., 28.

[7]. He K, Gkioxari G, Dollár P, et al. Mask r-cnn. 2017 Conf. Com. Vis. Pat. Rec.: 2961-2969.

[8]. Hariharan B, Arbeláez P, Girshick R, et al. Simultaneous detection and segmentation, 2014, Euro. Conf. Com. Vis.: 297-312.

[9]. Liu S, Qi X, Shi J, et al. Multi-scale patch aggregation (mpa) for simultaneous detection and segmentation. 2016 Conf. Com. Vis. Pat. Rec.3141-3149.

[10]. O Pinheiro P O, Collobert R, Dollár P. Learning to segment object candidates. Adv. Neu. Inf. Pro. Sys., 2015, 28.

[11]. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation 2015 Conf. Com. Vis. Pat. Rec. 3431-3440.

[12]. Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014.

[13]. Dai J, Qi H, Xiong Y, et al. Deformable convolutional networks. 2017 Conf. Com. Vis, 764-773.

[14]. Lin G, Milan A, Shen C, et al. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation 2017 Conf. Com. Vis. Pat. Rec. 1925-1934.

[15]. Saleh F, Aliakbarian M S, Salzmann M, et al. Built-in foreground/background prior for weakly-supervised semantic segmentation. 2016, Euro. Conf. Com. Vis. 413-432.

Cite this article

Fan,Y. (2023). Image semantic segmentation using deep learning technique. Applied and Computational Engineering,4,810-817.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Csurka G, Perronnin F. An efficient approach to semantic segmentation. 2011, Int. J. Com. Vis., 95(2): 198-212.

[2]. Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. 2006 Sci, 313(5786): 504-507.

[3]. Couprie C, Farabet C, Najman L, et al. Indoor semantic segmentation using depth information. 2013 arXiv preprint arXiv:1301.3572.

[4]. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014 Conf. Com. Vis. Pat. Rec. 580-587.

[5]. Girshick R. Fast r-cnn 2015 Conf. Com. Vis: 1440-1448.

[6]. Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. 2015 Adv. Neu. Inf. Pro. Sys., 28.

[7]. He K, Gkioxari G, Dollár P, et al. Mask r-cnn. 2017 Conf. Com. Vis. Pat. Rec.: 2961-2969.

[8]. Hariharan B, Arbeláez P, Girshick R, et al. Simultaneous detection and segmentation, 2014, Euro. Conf. Com. Vis.: 297-312.

[9]. Liu S, Qi X, Shi J, et al. Multi-scale patch aggregation (mpa) for simultaneous detection and segmentation. 2016 Conf. Com. Vis. Pat. Rec.3141-3149.

[10]. O Pinheiro P O, Collobert R, Dollár P. Learning to segment object candidates. Adv. Neu. Inf. Pro. Sys., 2015, 28.

[11]. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation 2015 Conf. Com. Vis. Pat. Rec. 3431-3440.

[12]. Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014.

[13]. Dai J, Qi H, Xiong Y, et al. Deformable convolutional networks. 2017 Conf. Com. Vis, 764-773.

[14]. Lin G, Milan A, Shen C, et al. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation 2017 Conf. Com. Vis. Pat. Rec. 1925-1934.

[15]. Saleh F, Aliakbarian M S, Salzmann M, et al. Built-in foreground/background prior for weakly-supervised semantic segmentation. 2016, Euro. Conf. Com. Vis. 413-432.

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