.png){kind=icon}
返回
Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2936176.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The procedure of establishing the correspondence between two sets of feature points is important in computer vision applications. In this article, an elastic net constraint-based tensor model is proposed for high-order graph matching. To control the tradeoff between the sparsity and the accuracy of the matching results, an elastic net constraint is introduced into the tensor-based graph matching model. Then, a nonmonotone spectral projected gradient (NSPG) method is derived to solve the proposed matching model. During the optimization of using NSPG, we propose an algorithm to calculate the projection on the feasible convex sets of elastic net constraint. Further, the global convergence of solving the proposed model using the NSPG method was proved. The superiority of the proposed method is verified through experiments on the synthetic data and natural images.
Keyword:
Optimization
Linear programming
Frequency modulation
Computational modeling
Approximation algorithms
Probabilistic logic
Elastic net
high-order graph matching
nonmonotone spectral projected gradient (NSPG)
tensor
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
Triangular Alignment (TAME): A Tensor-Based Approach for Higher-Order Network Alignment三角对齐 (TAME): 基于张量的高阶网络对齐方法
OAAI
OAAI分享收藏
查看更多内容