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Asian Conference on Computer Vision

ACCV 2014: Computer Vision -- ACCV 2014 pp 118–132Cite as

Efficient Image Detail Mining

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

Abstract

Two novel problems straddling the boundary between image retrieval and data mining are formulated: for every pixel in the query image, (i) find the database image with the maximum resolution depicting the pixel and (ii) find the frequency with which it is photographed in detail.

An efficient and reliable solution for both problems is proposed based on two novel techniques, the hierarchical query expansion that exploits the document at a time (DAAT) inverted file and a geometric consistency verification sufficiently robust to prevent topic drift within a zooming search.

Experiments show that the proposed method finds surprisingly fine details on landmarks, even those that are hardly noticeable for humans.

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Acknowledgement

The authors were supported by the MSMT LL1303 ERC-CZ, GACR P103/12/G084, and SGS13/142/OHK3/2T/13 grants.

Author information

Authors and Affiliations

  1. Center for Machine Perception, Department of Cybernetics, Faculty of EE, Czech Technical University in Prague, Prague, Czech Republic

    Andrej Mikulík, Filip Radenović, Ondřej Chum & Jiří Matas

Authors
  1. Andrej Mikulík
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  2. Filip Radenović
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  3. Ondřej Chum
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  4. Jiří Matas
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Corresponding author

Correspondence to Filip Radenović .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Mikulík, A., Radenović, F., Chum, O., Matas, J. (2015). Efficient Image Detail Mining. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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