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A Sparse Representation Based Classification Algorithm for Chinese Food Recognition

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Obesity is becoming a widely concerned health problem of most part of the world. Computer vision based recognition system has great potential to be an efficient tool to monitor food intake and cope with the growing problem of obesity. This paper proposes a food recognition algorithm based on sparse representation. The proposed algorithm learns overcomplete dictionaries from local descriptors including texture and color features that are extracted from food image patches. With the learned two overcomplete dictionaries, a feature vector of the food image can be generated with the sparsely encoded local descriptors. SVM is used for the classification. This research creates a Chinese food image dataset for experiments. Classifying Chinese food is more challenging because they are not as distinguishable visually as western food. The proposed algorithm achieves an average classification accuracy of 97.91% in a dataset of 5309 images that comprises 18 classes. The proposed method can be easily employed to dataset with more classes. Our results demonstrate the feasibility of using the proposed algorithm for food recognition.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China

    Haixiang Yang & Dong Zhang

  2. SYSU-CMU Shunde International Joint Research Institute, Shunde, China

    Haixiang Yang, Dong Zhang & Minjie Huang

  3. Department of Electrical and Computer Engineering, Brigham Young University, Provo, USA

    Dah-Jye Lee

Authors
  1. Haixiang Yang
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  2. Dong Zhang
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  3. Dah-Jye Lee
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  4. Minjie Huang
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Corresponding author

Correspondence to Haixiang Yang .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Yang, H., Zhang, D., Lee, DJ., Huang, M. (2016). A Sparse Representation Based Classification Algorithm for Chinese Food Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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