Fast Visual Vocabulary Construction for Image Retrieval Using Skewed-Split k-d Trees

  • Ilias GialampoukidisEmail author
  • Stefanos Vrochidis
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9516)


Most of the image retrieval approaches nowadays are based on the Bag-of-Words (BoW) model, which allows for representing an image efficiently and quickly. The efficiency of the BoW model is related to the efficiency of the visual vocabulary. In general, visual vocabularies are created by clustering all available visual features, formulating specific patterns. Clustering techniques are k-means oriented and they are replaced by approximate k-means methods for very large datasets. In this work, we propose a faster construction of visual vocabularies compared to the existing method in the case of SIFT descriptors, based on our observation that the values of the 128-dimensional SIFT descriptors follow the exponential distribution. The application of our method to image retrieval in specific image datasets showed that the mean Average Precision is not reduced by our approximation, despite that the visual vocabulary has been constructed significantly faster compared to the state of the art methods.


Visual Vocabulary Construction Image Retrieval Task SIFT Descriptors Mean Average Precision Fast Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ilias Gialampoukidis
    • 1
    Email author
  • Stefanos Vrochidis
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteCERTHThessalonikiGreece

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