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Global Evolution-Constructed Feature for Date Maturity Evaluation

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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

Evolution-Constructed (ECO) Feature as a method to learn image features has achieved very good results on a variety of object recognition and classification applications. When compared with hand-crafted features, ECO-Feature is capable of constructing non-intuitive features that could be overlooked by human experts. Although the ECO features are easy to compute, they are sensitive to small variation of object location and orientation in the images. This paper presents an improved ECO-Feature that addresses these limitations of the original ECO-Feature. The proposed method constructs a global representation of the object and also achieves invariance to small deformations. Two major changes are made in the proposed method to achieve good performance. A non-linear down-sampling technique is employed to reduce the dimensionality of the generated global features and hence improve the training efficiency of ECO-Feature. We apply the global ECO-Feature on a dataset of fruit date to demonstrate the improvement on the original ECO-Feature and the experimental results show the global ECO-Feature’s ability to generate better features for date maturity evaluation.

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Acknowledgement

The project was supported by the Small Business Innovation Research program of the U.S. Department of Agriculture, grant number #2015-33610-23786.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Brigham Young University, Provo, Utah, 84602, USA

    Meng Zhang & Dah-Jye Lee

Authors
  1. Meng Zhang
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  2. Dah-Jye Lee
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Corresponding author

Correspondence to Meng Zhang .

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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Zhang, M., Lee, DJ. (2016). Global Evolution-Constructed Feature for Date Maturity Evaluation. 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_27

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_27

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  • 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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