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SimCLR-Inception: An Image Representation Learning and Recognition Model for Robot Vision

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Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14406))

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Abstract

Effective feature extraction is a key component in image recognition for robot vision. This paper presents an improved contrastive learning-based image feature extraction and classification model, termed SimCLR-Inception, to realize effective and accurate image recognition. By using the SimCLR, this model generates positive and negative image samples from unlabeled data through image augmentation and then minimizes the contrastive loss function to learn the image representations by exploring more underlying structure information. Furthermore, this proposed model uses the Inception V3 model to classify the image representations for improving recognition accuracy. The SimCLR-Inception model is compared with four representative image recognition models, including LeNet, VGG16, Inception V3, and EfficientNet V2 on a real-world Multi-class Weather (MW) data set. We use four representative metrics: accuracy, precision, recall, and F1-Score, to verify the performance of different models for image recognition. We show that the presented SimCLR-Inception model achieves all the successful runs and gives almost the best results. The accuracy is at least \(4\%\) improved by the Inception V3 model. It suggests that this model would work better for robot vision.

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Notes

  1. 1.

    https://data.mendeley.com/datasets/4drtyfjtfy/1.

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Acknowledgement

We acknowledge the funding support from the National Natural Science Foundation of China (71974069).

Author information

Authors and Affiliations

  1. College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, People’s Republic of China

    Mengyuan Jin, Li Ma & Fang Hu

  2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Yin Zhang

  3. School of Information Management, Central China Normal University, Wuhan, 430079, People’s Republic of China

    Xiufeng Cheng

  4. Department of Mathematics and Statistics, University of West Florida, Pensacola, 32514, USA

    Fang Hu

Authors
  1. Mengyuan Jin
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  2. Yin Zhang
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  3. Xiufeng Cheng
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  4. Li Ma
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  5. Fang Hu
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Corresponding author

Correspondence to Fang Hu .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Jin, M., Zhang, Y., Cheng, X., Ma, L., Hu, F. (2023). SimCLR-Inception: An Image Representation Learning and Recognition Model for Robot Vision. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14406. Springer, Cham. https://doi.org/10.1007/978-3-031-47634-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-47634-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47633-4

  • Online ISBN: 978-3-031-47634-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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