.png){kind=icon}
返回
Subgraph learning for graph matching
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2018.07.005.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph matching is a powerful tool for computer vision, distance measure and machine learning. However, many factors influences the accuracy of matching. The outliers is a key problem in the process of matching. In this paper, a novel approach is proposed to handle graph matching problem based on Markov Chain Monte Carlo framework. By constructing a target distribution, the proposed can perform a process of sampling to maximize the graph matching objective. In this process, our method can effectively save matching pairwise under one-to-one matching constraints and also avoid the effect of outliers and deformation. The corresponding experiments on synthetic graphs, real images and view-based 3D model retrieval demonstrate the superiority of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Graph matching
Markov Chain Monte Carlo
Image matching
Object retrieval
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容