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Efficient Representation of Local Geometry for Large Scale Object Retrieval

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00163137" target="_blank" >RIV/68407700:21230/09:00163137 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Efficient Representation of Local Geometry for Large Scale Object Retrieval

  • Original language description

    State of the art methods for image and object retrieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems, memory becomes a limiting factor - local geometry is stored for each feature detected in each image and requires storage larger than the inverted file and term frequency and inverted document frequency weights together. We propose a novel method for learning discretized local geometry representation based on minimization of average reprojection error in the space of ellipses. The representation requires only 24 bits per feature without drop in performance. Additionally, we show that if the gravity vector assumption is used consistently from the feature description to spatial verification, it improves retrieval performance and decreases the memory footprint. The proposed method outperforms state of the art retrieval algorithms in a standard image retrieval benchmark.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA102%2F07%2F1317" target="_blank" >GA102/07/1317: Methods for Visual Recognition of Large Collections of Non-rigid Objects</a><br>

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2009

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    CVPR 2009: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

  • ISBN

    978-1-4244-3991-1

  • ISSN

    1063-6919

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

  • Publisher name

    Omnipress

  • Place of publication

    Madison

  • Event location

    Fontainebleau Resort, Miami Beach, Florida

  • Event date

    Jun 20, 2009

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article