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Deep Convolutional Neural Networks and Maximum-Likelihood Principle in Approximate Nearest Neighbor Search

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Pattern Recognition and Image Analysis (IbPRIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10255))

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Abstract

Deep convolutional neural networks are widely used to extract high-dimensional features in various image recognition tasks. If the count of classes is relatively large, performance of the classifier for such features can be insufficient to be implemented in real-time applications, e.g., in video-based recognition. In this paper we propose the novel approximate nearest neighbor algorithm, which sequentially chooses the next instance from the database, which corresponds to the maximal likelihood (joint density) of distances to previously checked instances. The Gaussian approximation of the distribution of dissimilarity measure is used to estimate this likelihood. Experimental study results in face identification with LFW and YTF datasets are presented. It is shown that the proposed algorithm is much faster than an exhaustive search and several known approximate nearest neighbor methods.

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Notes

  1. 1.

    https://github.com/HSE-asavchenko/HSE_FaceRec/tree/master/src/caffe_models.

  2. 2.

    https://github.com/HSE-asavchenko/HSE_FaceRec/tree/master/src/recognition_testing.

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Acknowledgements

The work is supported by Russian Federation President grant no. MД-306.2017.9 and Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics. The research in Sect. 2 was supported by RSF (Russian Science Foundation) project No. 14-41-00039.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

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  1. Andrey V. Savchenko
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Correspondence to Andrey V. Savchenko .

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Editors and Affiliations

  1. Universidade da Beira Interior , Covilhã, Portugal

    Luís A. Alexandre

  2. University Jaume I , Castellón, Spain

    José Salvador Sánchez

  3. University of the Algarve , Faro, Portugal

    João M. F. Rodrigues

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Savchenko, A.V. (2017). Deep Convolutional Neural Networks and Maximum-Likelihood Principle in Approximate Nearest Neighbor Search. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-58838-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58837-7

  • Online ISBN: 978-3-319-58838-4

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