A Deep Learning-Based Image Semantic Segmentation Algorithm


Chaoqun Shen, Zhongliang Sun, Journal of Information Processing Systems Vol. 19, No. 1, pp. 98-108, Feb. 2023  

10.3745/JIPS.02.0191
Keywords: Attention Mechanism, FCN, Image Semantic Segmentation, Skip Structure, VGG16
Fulltext:

Abstract

This paper is an attempt to design segmentation method based on fully convolutional networks (FCN) and attention mechanism. The first five layers of the Visual Geometry Group (VGG) 16 network serve as the coding part in the semantic segmentation network structure with the convolutional layer used to replace pooling to reduce loss of image feature extraction information. The up-sampling and deconvolution unit of the FCN is then used as the decoding part in the semantic segmentation network. In the deconvolution process, the skip structure is used to fuse different levels of information and the attention mechanism is incorporated to reduce accuracy loss. Finally, the segmentation results are obtained through pixel layer classification. The results show that our method outperforms the comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU).


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Cite this article
[APA Style]
Shen, C. & Sun, Z. (2023). A Deep Learning-Based Image Semantic Segmentation Algorithm. Journal of Information Processing Systems, 19(1), 98-108. DOI: 10.3745/JIPS.02.0191.

[IEEE Style]
C. Shen and Z. Sun, "A Deep Learning-Based Image Semantic Segmentation Algorithm," Journal of Information Processing Systems, vol. 19, no. 1, pp. 98-108, 2023. DOI: 10.3745/JIPS.02.0191.

[ACM Style]
Chaoqun Shen and Zhongliang Sun. 2023. A Deep Learning-Based Image Semantic Segmentation Algorithm. Journal of Information Processing Systems, 19, 1, (2023), 98-108. DOI: 10.3745/JIPS.02.0191.