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A Multi-view Images Classification Based on Shallow Convolutional Neural Network

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Advances in Brain Inspired Cognitive Systems (BICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11691))

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Abstract

Multi-view images represent the target object from multiple perspectives. Learning the target object information from different viewpoints helps to improve the accuracy of multi-view images classification. We propose a multi-view recognition method (SCNN-a) based on shallow convolutional neural network. In order to improve the generalization capability and classification performance of model, we develop a new multi-view images classification method (SCNN), which adds Dropout (after each max-pooling layer) technology to SCNN-a. SCNN-a and SCNN regard the images from predefined views as latent variables, and extract the high-order features of multi-view images with two convolutional layers. Experimental results show that SCNN achieves similar accuracy to the state-of-the-art result of [7] with less layers and time complexity.

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Acknowledgment

This research was funded by the Scientific and Technological Projects of Guangdong Province grant number (2017A050501039), the Guangdong Province General Colleges and Universities Featured Innovation grant number (2015GXJK080), the Qingyuan Science and Technology Plan Project grant number (170809111721-249, 170802171710591), and the Scientific and Technological Projects of Guangdong Province (2019A070701013).

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Authors and Affiliations

  1. School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou, 510640, China

    Fangyuan Lei, Xun Liu, Qingyun Dai, Lin Wang & Rongfu Zhou

  2. School of Computer, Guangdong Polytechnic Normal University, Guangzhou, 510640, China

    Huimin Zhao

Authors
  1. Fangyuan Lei
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  2. Xun Liu
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  3. Qingyun Dai
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  4. Huimin Zhao
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  5. Lin Wang
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  6. Rongfu Zhou
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Corresponding author

Correspondence to Fangyuan Lei .

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Jinchang Ren

  2. Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Guangdong Polytechnic Normal University, Guangzhou, China

    Huimin Zhao

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  5. Northwestern Polytechnical University, Xi'an, China

    Jiangbin Zheng

  6. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  7. Guangdong Polytechnic Normal University, Guangzhou, China

    Rongjun Chen

  8. Guangdong Polytechnic Normal University, Guangzhou, China

    Yinyin Xiao

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Lei, F., Liu, X., Dai, Q., Zhao, H., Wang, L., Zhou, R. (2020). A Multi-view Images Classification Based on Shallow Convolutional Neural Network. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-39431-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39430-1

  • Online ISBN: 978-3-030-39431-8

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