Higher-order clique reduction in binary graph cut
By Hiroshi Ishikawa. 2009
171 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Joost van de Weijer
- Andrew D. Bagdanov
- Stefan Roth
- Matthew F. Glasser
- Daniel Rueckert
- D. V. Van Essen
- Ben Glocker
- Konrad Schindler
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Jitendra Malik
- Xiaogang Wang
- Alan L. Yuille
- Dacheng Tao
- Bernt Schiele
- Andrew Zisserman
Citations in the corpus
- Optical flow modeling and computation: A survey (2015, background): “Dealing with higher-order local interactions between pixels has also been addressed in several recent works [86,128,144,150].”
- 3D Scene Flow Estimation with a Piecewise Rigid Scene Model (2015, background): “Over the years several reduction techniques havebeenproposed(e.g.,Ali et al. 2008; Ishikawa 2009; Rother et al. 2009).”
- Harmony Potentials (2011, background): “Other authors use high-order cliques in the original connectivity pattern, and then convert them into order two cliques by the introduction of new variables (Ramalingam et al, 2008; Rother et al, 2009; Ishikawa, 2009; Kohli and Kumar, 2010).”
- Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “This allows reduction of higher order terms either by: A) addition of auxiliary variables (for example by reducing a triplet to three pairwise terms (Ishikawa, 2009); or B) by reconfiguration of the polynomial form of the MRF energy, until the higher-order function can be replaced by a single…”