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A Clothing Classification Network with Manifold Structure Based on Second-Order Convolution

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International Conference on Neural Computing for Advanced Applications (NCAA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1870))

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Abstract

Currently, AI-based clothing image classification techniques mostly use traditional deep learning methods, which are based on monocular clothing images for classification. However, the diversity of perspectives of realistic clothing images bring great difficulties and challenges to clothing classification. Moreover, deep convolutional networks have limitations of their own. They treat data as vectors in Euclidean space and fail to make full use of the potential low dimensional non-linear geometric structure information within high-dimensional clothing image data. Therefore, this paper explores and exploits the geometric structure information inherent of clothing image data from the perspective of a non-Euclidean manifold learning method, and designs and implements a clothing classification network with manifold structure based on second-order convolution to classify images using the second-order statistics of clothing features for image classification. Firstly, the input clothing image features extracted by the convolution neural network are pooled with the covariance pooling module to obtain the second-order statistical covariance, which is converted into SPD manifold to characterize the feature information of the clothing image set, and then a complete manifold structure neural network is constructed to enhance the feature representation ability of the model on the geometric intrinsic structure of the clothing image set. The experimental results of this method on the multi view clothing image dataset MVC show that it has good effectiveness, robustness, and accuracy.

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Author information

Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, China

    Ruhan He

  2. Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion, Wuhan, China

    Cheng Quan

Authors
  1. Ruhan He
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  2. Cheng Quan
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Corresponding author

Correspondence to Cheng Quan .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Chaohu University, Hefei, China

    Yinggen Ke

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

  5. Hefei University of Technology, Hefei, China

    Zhao Zhang

  6. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  7. Chaohu University, Hefei, China

    Yuanyuan Mu

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He, R., Quan, C. (2023). A Clothing Classification Network with Manifold Structure Based on Second-Order Convolution. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_10

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  • DOI: https://doi.org/10.1007/978-981-99-5847-4_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5846-7

  • Online ISBN: 978-981-99-5847-4

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