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Multi-scale deep context convolutional neural networks for semantic segmentation

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Abstract

Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing the rich contextual information is very critical for dense pixel prediction. On the other hand, when going deeper in convolutional layers, the convolutional feature maps of traditional DCNNs gradually become coarser, which may be harmful for semantic segmentation. According to these observations, we attempt to design a multi-scale deep context convolutional network (MDCCNet), which combines the feature maps from different levels of network in a holistic manner for semantic segmentation. The segmentation outputs of MDCCNets are further enhanced using dense connected conditional random fields (CRF). The proposed network allows us to fully exploit local and global contextual information, ranging from an entire scene to every single pixel, to perform pixel-wise label estimation. The experimental results demonstrate that our method outperforms or is comparable to state-of-the-art methods on PASCAL VOC 2012 and SIFTFlow semantic segmentation datasets.

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Acknowledgements

The authors would like to thank all the anonymous reviewers for their valuable comments and suggestions. This work was partly supported by the National Science Foundation (Grant No. IIS-1302164), the National Natural Science Foundation of China (Grant No. 61401228, 61402238, 61762021, 61571240, 61501247, 61501259, 61671253, 61402122), China Postdoctoral Science Foundation (Grant No. 2015M581841), Natural Science Foundation of Jiangsu Province (Grant No. BK20150849, BK20160908), Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A), Open Research Fund of National Engineering Research Center of Communications and Networking (Nanjing University of Posts and Telecommunications) (Grant No. TXKY17009), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (Grant No. MJUKF201710), Open Fund Project of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology) (Grant No. JYB201709, JYB201710), Natural Science Foundation of Guizhou Province (Grant No.[2017]1130), and the 2014 Ph.D Recruitment Program of Guizhou Normal University.

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Authors and Affiliations

  1. National Engineering Research Center of Communications and Networking, Nanjing University of Posts, Telecommunications, Nanjing, China

    Quan Zhou & Wenbing Yang

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China

    Quan Zhou & Wenbing Yang

  3. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science, Technology, Nanjing, China

    Guangwei Gao

  4. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

    Weihua Ou

  5. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  6. Huawei Technologies Co. Ltd., ShenZhen, China

    Jie Chen

  7. Department of Computer and Information Sciences, Temple University, Philadelphia, USA

    Longin Jan Latecki

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  1. Quan Zhou
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  7. Longin Jan Latecki
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Correspondence to Quan Zhou or Weihua Ou.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications

Guest Editors: Jingkuan Song, Shuqiang Jiang, Elisa Ricci, and Zi Huang

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Zhou, Q., Yang, W., Gao, G. et al. Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web 22, 555–570 (2019). https://doi.org/10.1007/s11280-018-0556-3

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  • DOI: https://doi.org/10.1007/s11280-018-0556-3

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