Transformation of general binary mrf minimization to the first-order case

Hiroshi Ishikawa*

*この研究の対応する著者

研究成果: Article査読

78 被引用数 (Scopus)

抄録

We introduce a transformation of general higher-order Markov random field with binary labels into a first-order one that has the same minima as the original. Moreover, we formalize a framework for approximately minimizing higher-order multilabel MRF energies that combines the new reduction with the fusion-move and QPBO algorithms. While many computer vision problems today are formulated as energy minimization problems, they have mostly been limited to using first-order energies, which consist of unary and pairwise clique potentials, with a few exceptions that consider triples. This is because of the lack of efficient algorithms to optimize energies with higher-order interactions. Our algorithm challenges this restriction that limits the representational power of the models so that higher-order energies can be used to capture the rich statistics of natural scenes. We also show that some minimization methods can be considered special cases of the present framework, as well as comparing the new method experimentally with other such techniques.

本文言語English
論文番号5444874
ページ(範囲)1234-1249
ページ数16
ジャーナルIEEE Transactions on Pattern Analysis and Machine Intelligence
33
6
DOI
出版ステータスPublished - 2011

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 計算理論と計算数学
  • 人工知能
  • 応用数学

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