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Approximate Gaussian Mixtures for Large Scale Vocabularies

  • Yannis Avrithis
  • Yannis Kalantidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

We introduce a clustering method that combines the flexibility of Gaussian mixtures with the scaling properties needed to construct visual vocabularies for image retrieval. It is a variant of expectation-maximization that can converge rapidly while dynamically estimating the number of components. We employ approximate nearest neighbor search to speed-up the E-step and exploit its iterative nature to make search incremental, boosting both speed and precision. We achieve superior performance in large scale retrieval, being as fast as the best known approximate k-means.

Keywords

Gaussian mixtures expectation-maximization visual vocabularies large scale clustering approximate nearest neighbor search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yannis Avrithis
    • 1
  • Yannis Kalantidis
    • 1
  1. 1.National Technical University of AthensGreece

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