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Image Feature Extraction Using Compressive Sensing

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Image Processing and Communications Challenges 5

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 233))

Summary

In this paper a new approach for image feature extraction is presented. We used the Compressive Sensing (CS) concept to generate the measurement matrix. The new measurement matrix is different from the measurement matrices in literature as it was constructed using both zero mean and nonzero mean rows. The image is simply projected into a new space using the measurement matrix to obtain the feature vector. Another proposed measurement matrix is a random matrix constructed from binary entries. Face recognition problem was used as an example for testing the feature extraction capability of the proposed matrices. Experiments were carried out using two well-known face databases, namely, ORL and FERET databases. System performance is very promising and comparable with the classical baseline feature extraction algorithms.

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Author information

Authors and Affiliations

  1. Electrical and Electronics Engineering Dept., Mevlana University, Konya, Turkey

    Alaa Eleyan

  2. Electrical and Electronics Engineering Dept., Bilkent University, Ankara, Turkey

    Kivanc Kose & A. Enis Cetin

Authors
  1. Alaa Eleyan
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  2. Kivanc Kose
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  3. A. Enis Cetin
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Corresponding author

Correspondence to Alaa Eleyan .

Editor information

Editors and Affiliations

  1. Life Sciences, Institute of Telecommunications, University of Technology, ul.S.Kaliskiego 7, Bydgoszcz, 85791, Poland

    Ryszard S. Choras

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© 2014 Springer International Publishing Switzerland

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Eleyan, A., Kose, K., Cetin, A.E. (2014). Image Feature Extraction Using Compressive Sensing. In: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01622-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-01622-1_21

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01621-4

  • Online ISBN: 978-3-319-01622-1

  • eBook Packages: EngineeringEngineering (R0)

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