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VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder

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Pattern Recognition and Computer Vision (PRCV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13020))

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Abstract

Visual Place Recognition (VPR) is a challenging task in Visual Simultaneous Localization and Mapping (VSLAM), which expects to find out paired images corresponding to the same place in different conditions. Although most methods based on Convolutional Neural Network (CNN) perform well, they require a large number of annotated images for supervised training, which is time and energy consuming. Thus, to train the CNN in an unsupervised way and achieve better performance, we propose a new place recognition method in this paper. We design a VGG16-based Convolutional Autoencoder (VGG-CAE), which uses the features outputted by VGG16 as the label of images. In this case, VGG-CAE learns the latent representation from the label of images and improves the robustness against appearance and viewpoint variation. When deploying VGG-CAE, features are extracted from query images and reference images with post-processing, the Cosine similarities of features are calculated respectively and a matrix for feature matching is formed accordingly. To verify the performance of our method, we conducted experiments with several public datasets, showing our method achieves competitive results comparing to existing approaches.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos. U1913202, U1813205, U1713213, 61772508), CAS Key Technology Talent Program, Shenzhen Technology Project (nos. JCYJ20180507182610734, JSGG20191129094012321)

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhenyu Xu, Qieshi Zhang, Fusheng Hao, Ziliang Ren, Yuhang Kang & Jun Cheng

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China

    Zhenyu Xu, Qieshi Zhang & Jun Cheng

  3. The Chinese University of Hong Kong, Hong Kong, China

    Qieshi Zhang, Fusheng Hao, Ziliang Ren, Yuhang Kang & Jun Cheng

Authors
  1. Zhenyu Xu
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  2. Qieshi Zhang
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  3. Fusheng Hao
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  4. Ziliang Ren
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  5. Yuhang Kang
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  6. Jun Cheng
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Corresponding author

Correspondence to Qieshi Zhang .

Editor information

Editors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Huimin Ma

  2. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Tsinghua University, Beijing, China

    Changshui Zhang

  4. Zhejiang University, Hangzhou, China

    Fei Wu

  5. Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hunan University, Changsha, China

    Yaonan Wang

  7. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jianhuang Lai

  8. Beijing Jiaotong University, Beijing, China

    Yao Zhao

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Xu, Z., Zhang, Q., Hao, F., Ren, Z., Kang, Y., Cheng, J. (2021). VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-88007-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88006-4

  • Online ISBN: 978-3-030-88007-1

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