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Image Retrieval by Using Non-subsampled Shearlet Transform and Krawtchouk Moment Invariants

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

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Abstract

In this paper, we use non-subsampled shearlet transform (NSST) and Krawtchouk Moment Invariants (KMI) to realize image retrieval based on texture and shape features. Shearlet is a new sparse representation tool of multidimensional function, which provides a simple and efficient mathematical framework.We decompose the images by NSST. The directional subband coefficients are modeled by Generalized Gaussian Distribution (GGD). The distribution parameters are used to build texture feature vectors which are measured by Kullback–Leibler distance (KLD). Meanwhile, low-order KMI are employed to extract shape features which are measured by Euclidean distance (ED). Finally, the image retrieval is achieved based on weighted distance measurement. Experimental results show the proposed retrieval system can obtain the highest retrieval rate comparing with the methods based on DWT, Contourlet, NSCT and DT-CWT.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing, 210016, China

    Cheng Wan & Yiquan Wu

Authors
  1. Cheng Wan
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  2. Yiquan Wu
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Correspondence to Cheng Wan .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Wan, C., Wu, Y. (2015). Image Retrieval by Using Non-subsampled Shearlet Transform and Krawtchouk Moment Invariants. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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