资源分类
》 资源 》论文》A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields

A Primal-Dual Algorithm for Higher-Order Multilabel Markov Random Fields

2019-12-12 | |  117 |   63 |   0

Abstract

Graph cuts method such as α-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision. Higher-order Markov Random Fields (MRF’s), which are important for numerous applications, have proven to be very difficult, especially for multilabel MRF’s (i.e. more than 2 labels). In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF’s. Primal-dual methods provide guaranteed approximation bounds, and can exploit information in the dual variables to improve their efficiency. Our algorithm generalizes the PD3

上一篇：An Exemplar-based CRF for Multi-instance Object Segmentation

下一篇：Locally Optimized Product Quantization for Approximate Nearest Neighbor Search

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...