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http://dx.doi.org/10.5392/JKCA.2021.21.03.023

Post-processing Algorithm Based on Edge Information to Improve the Accuracy of Semantic Image Segmentation  

Kim, Jung-Hwan (숭실대학교 미디어학과)
Kim, Seon-Hyeok (숭실대학교 미디어학과)
Kim, Joo-heui (숭실대학교 미디어학과)
Choi, Hyung-Il (숭실대학교 미디어학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.3, 2021 , pp. 23-32 More about this Journal
Abstract
Semantic image segmentation technology in the field of computer vision is a technology that classifies an image by dividing it into pixels. This technique is also rapidly improving performance using a machine learning method, and a high possibility of utilizing information in units of pixels is drawing attention. However, this technology has been raised from the early days until recently for 'lack of detailed segmentation' problem. Since this problem was caused by increasing the size of the label map, it was expected that the label map could be improved by using the edge map of the original image with detailed edge information. Therefore, in this paper, we propose a post-processing algorithm that maintains semantic image segmentation based on learning, but modifies the resulting label map based on the edge map of the original image. After applying the algorithm to the existing method, when comparing similar applications before and after, approximately 1.74% pixels and 1.35% IoU (Intersection of Union) were applied, and when analyzing the results, the precise targeting fine segmentation function was improved.
Keywords
Computer Vision; Machine Learning; Deep Learning; Image Processing; Semantic Segmentation;
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1 Carlos H. Perdiguero, C. R. Cabrera, J. Roberto, and L. Sastre, "In pixels we trust: From Pixel Labeling to Object Localization and Scene Categorization," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 2018.
2 Yanhua Cheng, Rui Cai, Zhiwei Li, Xin Zhao, and Kaiqi Huang, "Locality-sensitive deconvolution networks with gated fusion for rgb-d indoor semantic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
3 Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid, "Refinenet: Multi-path refinement networks for high-resolution semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
4 Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE transactions on pattern analysis and machine intelligence, Vol.40, No.4, pp.834-848, 2017.   DOI
5 Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation," Proceedings of the European conference on computer vision (ECCV), 2018.
6 D. Marmanisac, K. Schindlerb, J. D. Wegnerb, S. Gallianib, M. Datcua, and U. Stillac, "Classification with an edge: Improving semantic image segmentation with boundary detection," ISPRS Journal of Photogrammetry and Remote Sensing, Vol.135, pp.158-172, 2018.   DOI
7 Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom, A Convolutional Neural Network for Modelling Sentences, Department of Computer Science University of Oxford, 2014.
8 Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep residual learning for image recognition," Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
9 Jianbo Jiao, Yunchao Wei, Zequn Jie, Honghui Shi, Rynson Lau, and Thomas S. Huang, "Geometry-Aware Distillation for Indoor Semantic Segmentation," Computer Vision and Pattern Recognition (CVPR), 2019.
10 Nathan Silberman, Derek HoiemPushmeet and KohliRob Fergus, "Indoor segmentation and support inference from rgbd images," European Conference on Computer Vision, Springer, Berlin, Heidelberg, 2012.
11 Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, and Roberto Cipolla, "Segmentation and recognition using structure from motion point clouds," European conference on computer vision, Springer, Berlin, Heidelberg, 2008.
12 Jing Liu, Yuhang Wang, Yong Li, Jun Fu, Jiangyun Li, and Hanqing Lu, "Collaborative deconvolutional neural networks for joint depth estimation and semantic segmentation," IEEE transactions on neural networks and learning systems, Vol.29, No.11, pp.5655-5666, 2018.   DOI
13 Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals, "Recurrent Neural Network Regularization," Under review as a conference paper at ICLR, 2015.
14 J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.