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ImageX - Explore and Search Local/Private Images

  • Nico Hezel
  • Kai Uwe Barthel
  • Klaus Jung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10705)

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.

Keywords

Image exploration Visualization CBIR Keyword search 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Visual Computing GroupHTW Berlin - University of Applied SciencesBerlinGermany

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