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Efficient Feature Coding Based on Auto-encoder Network for Image Classification

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

Local descriptor coding is one crucial step in traditional Bag of Words (BoW) framework for image categorization. However, the slow coding speed of previous methods is one limitation for applications in large scale problems. Recently, neural network based models have been widely applied in various classification tasks. Using neural network models for descriptor coding is straightforward and efficient due to their fast forward propagation. In this paper, we propose to use the Auto-Encoder (AE) network as a local descriptor coding block, and further embed AE network in the BoW framework for the purpose of image classification. To make the hidden activities of AE network to be both selective and sparse, we add an efficient and effective regularization term into the learning process of AE network, which can promote sparsity of the hidden layer for each input descriptor as well as the selectivity for each hidden node. By incorporating the AE network coding with the BoW framework, we can achieve better results and faster speeds than other state-of-the-art feature coding methods on Caltech101, Scene15 and UIUC 8-Sports databases.

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Notes

  1. 1.

    We utilized lib-linear toolkit [32] in this paper for SVM training.

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Acknowledgement

This work has been supported in part by the National Basic Research Program of China (973 Program) Grant 2012CB316302 and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA06040102).

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Guo-Sen Xie, Xu-Yao Zhang & Cheng-Lin Liu

Authors
  1. Guo-Sen Xie
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  2. Xu-Yao Zhang
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  3. Cheng-Lin Liu
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Corresponding author

Correspondence to Guo-Sen Xie .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Xie, GS., Zhang, XY., Liu, CL. (2015). Efficient Feature Coding Based on Auto-encoder Network for Image Classification. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_41

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_41

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-16865-4

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