Learning a Fast Emulator of a Binary Decision Process

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Learning a Fast Emulator of a Binary Decision
Process
Jan Šochman and Jirí Matas

• Center for Machine Perception
• Czech Technical University, Prague
• ACCV 2007, Tokyo, Japan

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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
• Viola-Jones (2001) real-time performance
• 2500 citations
• Schneiderman-Kanade (1998) - smaller error rates,
  but 1000x slower
• 250 citations

Time is implicitly considered as an important
characteristic of detection and recognition
algorithms
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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

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Black-box Generated Training Set

• Emulator approximates behavior of the black-box
  algorithm
• The black-box algorithm can potentially provide
  almost unlimited number of training samples
• Efficiency of training is important
• Suitable for incremental or online methods

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WaldBoost Optimization Task

• Basic notions
• Sequential strategy S is characterized by
• Problem formulation

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WaldBoost Sequential Classifier Training

• Combines AdaBoost training (provides
  measurements) with Walds sequential decision
  making theory (for sequential decisions)
• Sequential WaldBoost classifier
• Set of weak classifiers (features)
  and
  thresholds are
  found during training

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Emulation of Similarity-Invariant Regions

• The approach tested on Hessian-Laplace 1 and
  Kadir-Brady salient 2 interest point detectors

• Motivation
• Hessian-Laplace is close to state of the art
• Kadirs 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

1 K. Mikolajczyk, C. Schmid. Scale and Affine
Invariant Interest Point Detectors. ICCV
2004. 2 T. Kadir, M. Brady. Saliency, Scale and
Image Description. IJCV 2001.
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Results Hessian-Laplace boat sequence

• Repeatability comparable (left graph)
• Matching score almost identical (middle graph)
• Higher number of correspondences and correct
  matches in WaldBoost.
• Speed comparison (850x680 image)

Original 1.3 sec
WaldBoost 1.3 sec
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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)

Original 1 min 44 sec
WaldBoost 1.4 sec
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Approximation Quality Hesian-Laplace

• Yellow circles repeated detections (85
  coverage)
• Red circles original detections not found by the
  WaldBoost detector

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Approximation Quality Kadir-Brady

• Yellow circles repeated detections (96
  coverage)
• Red circles original detections not found by the
  WaldBoost detector

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Conclusions and Future Work

• A general framework for speeding up existing
  binary decision algorithms has been presented
• To optimize the emulators 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?

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Conclusions and Future Work

• A general framework for speeding up existing
  binary decision algorithms has been presented
• To optimize the emulators 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