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Learning Fast Emulators of Binary Decision Processes

Result description

We shows how existing binary decision algorithms can be approximated by a fast trained WaldBoost classifier. WaldBoost learning minimises the decision time of the classifier while guaranteeing predefined precision. The WaldBoost algorithm together with bootstrapping is able to efficiently handle an effectively unlimited number of training examples provided by the implementation of the approximated algorithm. Two interest point detectors, the Hessian-Laplace and the Kadir-Brady saliency detectors, are emulated to demonstrate the approach. Experiments show that while the repeatability and matching scores are similar for the original and emulated algorithms, a 9-fold speed-up for the Hessian-Laplace detector and a 142-fold speed-up for the Kadir-Brady detector is achieved.

Keywords

BoostingAdaBoostSequential probability ratio testSequential decision makingWaldBoostInterest point detectorsMachine learning

The result's identifiers

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning Fast Emulators of Binary Decision Processes

  • Original language description

    We shows how existing binary decision algorithms can be approximated by a fast trained WaldBoost classifier. WaldBoost learning minimises the decision time of the classifier while guaranteeing predefined precision. The WaldBoost algorithm together with bootstrapping is able to efficiently handle an effectively unlimited number of training examples provided by the implementation of the approximated algorithm. Two interest point detectors, the Hessian-Laplace and the Kadir-Brady saliency detectors, are emulated to demonstrate the approach. Experiments show that while the repeatability and matching scores are similar for the original and emulated algorithms, a 9-fold speed-up for the Hessian-Laplace detector and a 142-fold speed-up for the Kadir-Brady detector is achieved.

  • Czech name

  • Czech description

Classification

  • Type

    Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

Others

  • Publication year

    2009

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    International Journal of Computer Vision

  • ISSN

    0920-5691

  • e-ISSN

  • Volume of the periodical

    83

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    15

  • Pages from-to

  • UT code for WoS article

    000264520400003

  • EID of the result in the Scopus database

Result type

Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

Jx

CEP

JD - Use of computers, robotics and its application

Year of implementation

2009