Learning a Fast Emulator of a Binary Decision Process Center for Machine Perception


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Learning a Fast Emulator of a Binary Decision Process

  • Center for Machine Perception

  • Czech Technical University, Prague

  • ACCV 2007, Tokyo, Japan


Importance of Classification Speed

  • Time-to-decision vs. precision trade-off is inherent in many detection, recognition and matching problems in computer vision

  • Often the trade-off is not explicitly stated in the problem formulation, but decision time clearly influences impact of a method

  • Example: face detection



Fast Emulation of A Decision Process

  • The Idea

  • Given a black box algorithm A performing some useful binary decision task

  • Train a sequential classifier S to (approximately) emulate detection performance of algorithm A while minimizing time-to-decision

  • Allow user to control quality of the approximation

  • Contribution

  • A general framework for speeding up existing algorithms by a sequential classifier learned by the WaldBoost algorithm [1]

  • Demonstrated on two interest point detectors

  • Advantages

  • Instead of spending man-months on code optimization, choose relevant feature class and train sequential classifier S

  • Your (slow) Matlab code can be speeded up this way!

  • [1] J. Šochman and J. Matas. Waldboost – Learning For Time Constrained Sequential Detection. CVPR 2005



Black-box Generated Training Set



WaldBoost Optimization Task

  • Basic notions:

  • Sequential strategy S is characterized by:

  • Problem formulation:



WaldBoost Sequential Classifier Training

  • Combines AdaBoost training (provides measurements) with Wald’s sequential decision making theory (for sequential decisions)

  • Sequential WaldBoost classifier

  • Set of weak classifiers (features) and thresholds are found during training



Emulation of Similarity-Invariant Regions

  • Motivation

  • Hessian-Laplace is close to state of the art

  • Kadir’s detector very slow (100x slower than Difference of Gaussians)

  • Standard testing protocol exists

  • Executables of both methods available at robots.ox.ac.uk

  • Both detectors are scale-invariant which is easily implemented via a scanning window + a sequential test

  • Implementation

  • Choice of : various filters computable with integral images - difference of rectangular regions, variance in a window

  • Positive samples: Patches twice the size of original interest point scale



Results: Hessian-Laplace – boat sequence



Results: Kadir-Brady – east-south sequence

  • Repeatability slightly higher (left graph)

  • Matching score slightly higher (middle graph)

  • Higher number of correspondences and correct matches in WaldBoost.

  • Speed comparison (850x680 image)



Approximation Quality – Hesian-Laplace



Approximation Quality – Kadir-Brady

  • Yellow circles: repeated detections (96% coverage)

  • Red circles: original detections not found by the WaldBoost detector



Conclusions and Future Work

  • A general framework for speeding up existing binary decision algorithms has been presented

  • To optimize the emulator’s time-to-decision a WaldBoost sequential classifier was trained

  • The approach was demonstrated on (but is not limited to) two interest point detectors emulation

  • Future work

  • Precise localization of the detections by interpolation on the grid

  • Using real value output of the black-box algorithm (“sequential regression”)

  • To emulate or to be repeatable?



Conclusions and Future Work

  • A general framework for speeding up existing binary decision algorithms has been presented

  • To optimize the emulator’s time-to-decision a WaldBoost sequential classifier was trained

  • The approach was demonstrated (but is not limited to) on two interest point detectors

  • Future work

  • Precise localization of the detections by interpolation on the grid

  • Using real value output of the black-box algorithm (“sequential regression”)

  • To emulate or to be repeatable?

  • Thank you for your attention




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