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Related Experiment Video

Updated: Jul 19, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Convergent tree-reweighted message passing for energy minimization.

Vladimir Kolmogorov¹

¹University College London, Adastral Park, Martlesham Heath, IP5 3RE, UK. vnk@adastral.ucl.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 22, 2006

Summary

We introduce sequential tree-reweighted message passing, an improved algorithm for discrete energy minimization in computer vision. This method guarantees bound non-decrease and converges, outperforming existing techniques like tree-reweighted max-product message passing.

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Last Updated: Jul 19, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Computer Vision
Optimization Algorithms

Background:

Discrete energy minimization is crucial in computer vision.
Existing tree-reweighted max-product message passing (TRW) algorithms may not guarantee bound increase or convergence.

Purpose of the Study:

To develop a modified message passing algorithm with guaranteed bound non-decrease and convergence.
To improve upon the performance and memory efficiency of existing discrete energy minimization techniques.

Main Methods:

Sequential tree-reweighted message passing (TRW) algorithm development.
Analysis of weak tree agreement for characterizing local maxima.
Comparative experimental evaluation against belief propagation and TRW [33].

Main Results:

The proposed algorithm guarantees that the energy bound does not decrease.
A weak tree agreement condition is established, and the algorithm is proven to achieve it.
The new algorithm requires half the memory of traditional message passing methods.
Experimental results show superior performance over ordinary belief propagation and TRW [33] on synthetic and real problems.
Achieved lower energy than graph cuts on stereo problems with Potts interactions.

Conclusions:

Sequential tree-reweighted message passing offers a robust and efficient alternative for discrete energy minimization.
The algorithm provides theoretical guarantees and practical advantages in terms of performance and memory usage.
This advancement has significant implications for various computer vision applications requiring energy minimization.