Multi-level graph convolutional recurrent neural network for semantic image segmentation

Dingchao Jiang (), Hua Qu (), Jihong Zhao (), Jianlong Zhao () and Wei Liang ()
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Dingchao Jiang: Xi’an Jiaotong University
Hua Qu: Xi’an Jiaotong University
Jihong Zhao: Xi’an University of Posts and Telecommunications
Jianlong Zhao: Xi’an Jiaotong University
Wei Liang: Hunan University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2021, vol. 77, issue 3, No 8, 563-576

Abstract: Abstract With the advent of the Internet of Things (IoT) era, many devices have surfaced that capture and generate various visual data. To recognize and extract a meaningful pattern from these visual data, powerful methods are required for different IoT applications. Fortunately, deep convolutional neural networks (CNNs) significantly improve the performance of almost all tasks in computer vision, including semantic image segmentation. However, the feature extraction of CNNs may cause the loss of contextual and spatial information. Moreover, the standard convolutional and pooling layers adopted by most CNN architectures lead to a fixed receptive field, which makes it challenging to deal with multi-scale objects in the image. To remedy these issues of CNNs for semantic image segmentation, this paper proposes a multi-level graph convolutional recurrent neural network (MGCRNN) to combine CNNs and graph neural networks (GNNs) for fusing multi-level features. By applying graph convolutional recurrent neural network (GCRNN), the proposed model acquires a global view of the image and aggregates multi-level contextual and structural information. The experiments verify the ability of GCRNN to obtain a flexible receptive field and learn structure features without losing spatial information. Results of these experiments conducted on the Pascal VOC 2012 and Cityscapes datasets show that the proposed model outperforms baseline approaches and can be competitive with state-of-the-art methods

Keywords: Deep learning; Semantic image segmentation; Graph convolutional recurrent neural network; Multi-level features (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11235-021-00769-y

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