scholarly journals Context-Dependent Random Walk Graph Kernels and Tree Pattern Graph Matching Kernels With Applications to Action Recognition

2018 ◽  
Vol 27 (10) ◽  
pp. 5060-5075 ◽  
Author(s):  
Weiming Hu ◽  
Baoxin Wu ◽  
Pei Wang ◽  
Chunfeng Yuan ◽  
Yangxi Li ◽  
...  
Keyword(s):  
Random Walk ◽  
Action Recognition ◽  
Graph Matching ◽  
Graph Kernels ◽  
Tree Pattern ◽  
Context Dependent ◽  
Pattern Graph

Neural Graph Matching Networks for Fewshot 3D Action Recognition

2018 ◽  
pp. 673-689 ◽  
Author(s):  
Michelle Guo ◽  
Edward Chou ◽  
De-An Huang ◽  
Shuran Song ◽  
Serena Yeung ◽  
...  
Keyword(s):  
Action Recognition ◽  
Graph Matching ◽  
Matching Networks

Incorporating Discrete Constraints Into Random Walk-Based Graph Matching

2020 ◽  
Vol 50 (4) ◽  
pp. 1406-1416 ◽  
Author(s):  
Xu Yang ◽  
Zhi-Yong Liu ◽  
Hong Qiaoxu
Keyword(s):  
Random Walk ◽  
Graph Matching

scholarly journals Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

2017 ◽  
Vol 7 (1.1) ◽  
pp. 489 ◽  
Author(s):  
P V.V. Kishore ◽  
P Siva Kameswari ◽  
K Niharika ◽  
M Tanuja ◽  
M Bindu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Graph Kernels ◽  
Joint Distributions ◽  
Spatial Graph ◽  
3D Space ◽  
Machine Interface ◽  
Action Frame

Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we propose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint distances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are constructed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.

A Comparative Study of Graph Kernels and Clustering Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 33-48
Author(s):  
Riju Bhattacharya ◽  
Naresh Kumar Nagwani ◽  
Sarsij Tripathi
Keyword(s):  
Random Walk ◽  
Comparative Study ◽  
Shortest Path ◽  
Kernel Method ◽  
Graph Clustering ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Graph Kernels ◽  
Silhouette Index

Graph kernels have evolved as a promising and popular method for graph clustering over the last decade. In this work, comparative study on the five standard graph kernel techniques for graph clustering has been performed. The graph kernels, namely vertex histogram kernel, shortest path kernel, graphlet kernel, k-step random walk kernel, and Weisfeiler-Lehman kernel have been compared for graph clustering. The clustering methods considered for the kernel comparison are hierarchical, k-means, model-based, fuzzy-based, and self-organizing map clustering techniques. The comparative study of kernel methods over the clustering techniques is performed on MUTAG benchmark dataset. Clustering performance is assessed with internal validation performance parameters such as connectivity, Dunn, and the silhouette index. Finally, the comparative analysis is done to facilitate researchers for selecting the appropriate kernel method for effective graph clustering. The proposed methodology elicits k-step random walk and shortest path kernel have performed best among all graph clustering approaches.

Random Walk Erasing with Attention Calibration for Action Recognition

2021 ◽  
pp. 236-251
Author(s):  
Yuze Tian ◽  
Xian Zhong ◽  
Wenxuan Liu ◽  
Xuemei Jia ◽  
Shilei Zhao ◽  
...  
Keyword(s):  
Random Walk ◽  
Action Recognition

scholarly journals Graph-Based Node Finding in Big Complex Contextual Social Graphs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Keshou Wu ◽  
Guanfeng Liu ◽  
Junwen Lu
Keyword(s):  
Social Trust ◽  
Graph Matching ◽  
Search Algorithm ◽  
Social Contexts ◽  
Graph Pattern Matching ◽  
Monte Carlo Tree Search ◽  
Graph Pattern ◽  
The Social ◽  
Efficiency And Effectiveness ◽  
Pattern Graph

Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches. The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS. The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

scholarly journals Human action recognition using graph matching

2019 ◽  
Author(s):  
Ashwan A. Abdulmunem ◽  
Yu-Kun Lai ◽  
Ahmed K. Hassan ◽  
Xianfang Sun
Keyword(s):  
Action Recognition ◽  
Graph Matching ◽  
Human Action Recognition ◽  
Human Action
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