.png){kind=icon}
返回
Error-tolerant approximate graph matching utilizing node centrality information
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2020.03.019.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 the task of finding the similarity between the two graphs. Error-tolerant graph matching is the process of computing the similarity between the two graphs, where some flexibility to noise or error is allowed to approximate the value of graph matching. In this paper, we present a framework for graph matching by utilizing the centrality measures to ignore the least central nodes of the graphs. Experimental evaluation shows that this approach can be useful to reduce the overall matching time and it can provide time versus accuracy trade-off. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Centrality measures
Graph edit distance
Graph matching
Structural pattern recognition
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}
Does it take older adults longer than younger adults to perceptually segregate a speech target from a background masker?在感知上将语音目标与背景掩蔽器隔离开来是否需要老年人比年轻人更长的时间？
PREAI
分享收藏
分享收藏
查看更多内容