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ImageX - Explore and Search Local/Private Images

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MultiMedia Modeling (MMM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10705))

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Abstract

In this paper we present a system to visually explore and search large sets of untagged images, running on common operating systems and consumer hardware. High quality image descriptors are computed using activations of a convolutional neural network. By applying normalization and a principal component analysis of the activations compact feature vectors of only 64 bytes are generated. The L1-distances for these feature vectors can be calculated very fast using a novel computation approach and allows search-by-example queries to be processed in fractions of a second. We further show how entire image collections can be transferred into hierarchical image graphs and describe a scheme to explore this complex data structure in an intuitive way. To enable keyword search for untagged images, reference features for common keywords are generated. These features are constructed by collecting and clustering examples images from the web.

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References

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Author information

Authors and Affiliations

  1. Visual Computing Group, HTW Berlin - University of Applied Sciences, Wilhelminenhofstraße 75a, 12459, Berlin, Germany

    Nico Hezel, Kai Uwe Barthel & Klaus Jung

Authors
  1. Nico Hezel
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  2. Kai Uwe Barthel
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  3. Klaus Jung
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Corresponding author

Correspondence to Nico Hezel .

Editor information

Editors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Klaus Schoeffmann

  2. Chulalongkorn University, Bangkok, Thailand

    Thanarat H. Chalidabhongse

  3. City University of Hong Kong, Hong Kong, China

    Chong Wah Ngo

  4. Chulalongkorn University, Bangkok, Thailand

    Supavadee Aramvith

  5. Dublin City University, Dublin, Ireland

    Noel E. O’Connor

  6. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  7. Tampere University of Technology, Tampere, Finland

    Moncef Gabbouj

  8. Rutgers University, Piscataway, New Jersey, USA

    Ahmed Elgammal

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Hezel, N., Barthel, K.U., Jung, K. (2018). ImageX - Explore and Search Local/Private Images. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_35

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  • DOI: https://doi.org/10.1007/978-3-319-73600-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73599-3

  • Online ISBN: 978-3-319-73600-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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