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A Hybrid Generative-Discriminative Learning Algorithm for Image Recognition

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Advanced Computational Methods in Life System Modeling and Simulation (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 761))

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Abstract

Feature representation is usually a key point in image recognition. The recognition performance can be potentially improved if the data distribution information is exploited. In this paper, we propose an image recognition approach based on generative score space. Specifically, we first leverage probabilistic latent semantic analysis (pLSA) to model the distribution of images. Then, we derive the mid-level feature from the model in a generative feature learning manner. At last, the derived feature is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the probabilistic generative modeling allows us exploiting information hidden in data and has good adaptation to data distribution. Second, the discriminative learning process can utilize the information of label effectively. To confirm the effectiveness of our method, we perform image recognition on three datasets. The results demonstrate its advantages.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61503251), the Science and Technology Commission of Shanghai Municipality (No. 16070502900), the Program of Shanghai Normal University (No. A-7001-15-001005).

Author information

Authors and Affiliations

  1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China

    Bin Wang, Chuanjiang Li & Hongwei Mao

  2. National Computer Network Emergency Response Technical Team, Beijing, 100029, China

    Xiong Li

Authors
  1. Bin Wang
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  2. Chuanjiang Li
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  3. Xiong Li
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  4. Hongwei Mao
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Corresponding author

Correspondence to Chuanjiang Li .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Minrui Fei

  2. Shanghai University, Shanghai, China

    Shiwei Ma

  3. Shanghai University, Shanghai, China

    Xin Li

  4. Shanghai University, Shanghai, China

    Xin Sun

  5. Shanghai University, Shanghai, China

    Li Jia

  6. Shanghai University, Shanghai, China

    Zhou Su

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© 2017 Springer Nature Singapore Pte Ltd.

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Wang, B., Li, C., Li, X., Mao, H. (2017). A Hybrid Generative-Discriminative Learning Algorithm for Image Recognition. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_46

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  • DOI: https://doi.org/10.1007/978-981-10-6370-1_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6369-5

  • Online ISBN: 978-981-10-6370-1

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