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Related Experiment Video

Updated: Jun 21, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

P3 & beyond: move making algorithms for solving higher order functions.

Pushmeet Kohli1, M Pawan Kumar, Philip H S Torr

  • 1Microsoft Research, Cambridge, UK. pkohli@microsoft.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 4, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces novel higher-order clique potentials for energy functions, enabling efficient computation of optimal alpha-expansion and alpha beta-swap moves. These advancements aid complex computer vision tasks like image segmentation.

Related Experiment Videos

Last Updated: Jun 21, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Computer Vision
  • Optimization Theory
  • Graph Theory

Background:

  • Energy minimization is crucial in computer vision.
  • Existing methods struggle with higher-order clique potentials.
  • Efficient algorithms are needed for complex energy functions.

Purpose of the Study:

  • Extend energy functions solvable by alpha-expansion and alpha beta-swap.
  • Introduce novel higher-order clique potentials.
  • Enable efficient minimization for complex energy models.

Main Methods:

  • Introduced a novel family of higher-order clique potentials.
  • Demonstrated that minimizing energy functions with these potentials involves submodular function minimization.
  • Identified a subset, the {\cal P};n Potts model, solvable via st-mincut.

Main Results:

  • Established polynomial-time computation for optimal alpha-expansion and alpha beta-swap moves for a broader class of energy functions.
  • Showcased the applicability of these methods to texture-based image and video segmentation.
  • Enabled the use of powerful move-making algorithms for higher-order clique energy minimization.

Conclusions:

  • The proposed higher-order clique potentials enhance energy function modeling capabilities.
  • These methods offer significant advantages for computer vision applications.
  • Efficiently solving complex energy minimization problems is now feasible.