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Multiple Stage Residual Model for Accurate Image Classification

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

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Abstract

Image classification is an important topic in computer vision. As a key procedure, encoding the local features to get a compact representation for image affects the final classification accuracy largely. There is no doubt that encoding procedure leads to information loss, due to the existence of quantization error. The residual vector, defined as the difference between the local image feature and its corresponding visual word, is the chief culprit that should be responsible for the quantization error. Many previous algorithms consider it as a coding issue, and focus on reducing the quantization error by reconstructing the feature with more than one visual words, or by the so-called soft-assignment strategy. In this paper, we consider the problem from a different view, and propose an effective and efficient model, which is called Multiple Stage Residual Model (MSRM), to make full use of the residual vector to generate a multiple stage code. Our proposed model is a generic framework, which can be built upon many coding algorithms and improves the image classification performance of the coding algorithms significantly. The experimental results on the image classification benchmarks, such as UIUC 8-Sport, Scene-15, Caltech-101 image dataset, confirm the validity of MSRM.

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Acknowledgement

This work was primarily supported by National Natural Science Foundation of China (NSFC) (No. 61222308), and in part by NSFC (No. 61173120), Program for New Century Excellent Talents in University (No. NCET-12-0217), Fundamental Research Funds for the Central Universities (No. HUST 2013TS115). X.Wang was supported by Microsoft Research Asia Fellowship 2012.

Author information

Authors and Affiliations

  1. Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

    Song Bai, Xinggang Wang, Cong Yao & Xiang Bai

Authors
  1. Song Bai

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  2. Xinggang Wang

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  3. Cong Yao

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  4. Xiang Bai

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Corresponding author

Correspondence toXiang Bai.

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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Bai, S., Wang, X., Yao, C., Bai, X. (2015). Multiple Stage Residual Model for Accurate 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_28

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