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Mining Graph Topological Patterns: Finding Covariations among Vertex Descriptors

Adriana Prado, Marc Plantevit, Celine Robardet, Jean-Francois Boulicaut
2013 IEEE Transactions on Knowledge and Data Engineering  
In this article, we propose to mine the graph topology of a large attributed graph by finding regularities among vertex descriptors.  ...  Such pattern mining task relies on frequent pattern mining and graph topology analysis to reveal the links that exist between the relation encoded by the graph and the vertex attributes.  ...  CONCLUSION AND FUTURE DIRECTIONS We propose TopGraphMiner, an algorithm that supports network analysis by finding regularities among vertex topological properties and attributes.  ... 
doi:10.1109/tkde.2012.154 fatcat:2b3odgo4efdjfic4eq7mg3tlpi

Supporting the Discovery of Relevant Topological Patterns in Attributed Graphs

Julien Salotti, Marc Plantevit, Celine Robardet, Jean-Francois Boulicaut
2012 2012 IEEE 12th International Conference on Data Mining Workshops  
A topological pattern is defined as a set of vertex attributes and topological properties (i.e., properties that characterize the role of a vertex within a graph) that strongly co-vary over the vertices  ...  We propose TopGraphVisualizer, a tool to support the discovery of relevant topological patterns in attributed graphs.  ...  It enables to find out the patterns among the publications target and some topological properties that are the most correlated to the graph structure.  ... 
doi:10.1109/icdmw.2012.38 dblp:conf/icdm/SalottiPRB12 fatcat:b3bwb5fnufe7pfgieva77vjzem

Context Aware Graph Convolution for Skeleton-Based Action Recognition

Xikun Zhang, Chang Xu, Dacheng Tao
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Besides the computation of localized graph convolution, CA-GCN considers a context term for each vertex by integrating information of all other vertices.  ...  Long range dependencies among joints are thus naturally integrated in context information, which then eliminates the need of stacking multiple layers to enlarge receptive field and greatly simplifies the  ...  For example, covariance matrix of joint locations over time is used in [11] as a discriminative descriptor for a skeleton sequence. [39] used an actionlet ensemble obtained by data mining to represent  ... 
doi:10.1109/cvpr42600.2020.01434 dblp:conf/cvpr/0002XT20 fatcat:7bzizis4nvefzmxyixcy6dt2te

A Survey of Shape Feature Extraction Techniques [chapter]

Yang Mingqiang, Kpalma Kidiyo, Ronsin Joseph
2008 Pattern Recognition Techniques, Technology and Applications  
Shock graphs Shock graphs is a descriptor based on the medial axis.  ...  Region moments Among the region-based descriptors, moments are very popular. These include invariant moments, Zernike moments Radial Chebyshev moments, etc.  ...  The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically  ... 
doi:10.5772/6237 fatcat:ggyqwzt4jvfuxg4cjb7vjniu3q

Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition [article]

Yonatan Tariku Tesfaye
2018 arXiv   pre-print
This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem.  ...  [156] consider a similar idea and find repetitive patterns among features to place recognition. Similarly, Hao et al.  ...  Moreover, we decided to model people appearance using covariance matrix feature descriptors [118] .  ... 
arXiv:1802.02181v1 fatcat:snsch56pdjglro2h32lhh5r64e

An algorithm for 3D shape matching using spherical sectioning

Jaeho Lee, Joonyoung Park, Hyunchan Lee
2006 Journal of Zhejiang University: Science A  
The techniques developed for a particular domain will also find application in other domains. We propose a new shape matching method.  ...  (a) 3D Model; (b) Bounding sphere; (c) Principal axis; (d) Spherical sections; (e) Rotational intersection planes; (f) Intersection patterns; (g) Vertex count (shape histogram); (h) Railroad table (normalized  ...  By computing the eigenvectors of a 3×3 covariance matrix, the direction vectors for a good-fit box can be taken.  ... 
doi:10.1631/jzus.2006.a1508 fatcat:eizsnkswbzcpno2i56g47gbg3u

Connecting the Dots: Identifying Network Structure via Graph Signal Processing [article]

Gonzalo Mateos, Santiago Segarra, Antonio G. Marques, Alejandro Ribeiro
2018 arXiv   pre-print
This tutorial offers an overview of graph learning methods developed to bridge the aforementioned gap, by using information available from graph signals to infer the underlying graph topology.  ...  Network topology inference is a prominent problem in Network Science.  ...  among vertex processes.  ... 
arXiv:1810.13066v1 fatcat:7ub2dgol7vhtxnwwgelficghz4

Multi-hop assortativities for networks classification [article]

Leonardo Gutierrez Gomez, Jean-Charles Delvenne
2018 arXiv   pre-print
Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata  ...  In complex networks, is natural to find diversity in mixing patterns arise from diverse nodes attributes.  ...  We define multi-hop assortativities by setting up a dynamic on the network and computing covariances over diverse node attributes among multiples time scales.  ... 
arXiv:1809.06253v1 fatcat:fgynj2jqtfhjjn3wzeuf2texdq

A Literature Review: Geometric Methods and Their Applications in Human-Related Analysis

Wenjuan Gong, Bin Zhang, Chaoqi Wang, Hanbing Yue, Chuantao Li, Linjie Xing, Yu Qiao, Weishan Zhang, Faming Gong
2019 Sensors  
Geometric features, such as the topological and manifold properties, are utilized to extract geometric properties.  ...  Laplacian eigenmaps [61] use graphs to find the embedding of the data in a low-dimensional space.  ...  Constructed graphs are decomposed into substructures called subgraphs, and these subgraphs are compared based on a proposed graph kernel named the subgraph-pattern graph kernel (SPGK).  ... 
doi:10.3390/s19122809 fatcat:nrnq4vrj7bfv7iv4aem5g6k4zu

Age Synthesis and Estimation via Faces: A Survey

Yun Fu, Guodong Guo, T S Huang
2010 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Human age, as an important personal trait, can be directly inferred by distinct patterns emerging from the facial appearance.  ...  In the age estimation stage, the test face image needs to find an aging pattern suitable for it and a proper age position in that aging pattern.  ...  The effective texture descriptor, Local Binary Patterns (LBP) [141] , has been used for appearance feature extraction in an automatic age estimation system [142] .  ... 
doi:10.1109/tpami.2010.36 pmid:20847387 fatcat:fzucchc7mzhanl7n3lsy6sxhe4

A Review on Deep Learning Approaches for 3D Data Representations in Retrieval and Classifications

Abubakar Sulaiman Gezawa, Yan Zhang, Qicong Wang, Lei Yunqi
2020 IEEE Access  
According to the findings in this work, multi views methods surpass voxel-based methods and with increased layers and enough data augmentation the performance can still be increased.  ...  The network learns from geometric topology constraint among individual points.  ...  model with latent variables to automatically find the context among multi-view images in both the spatial and feature domains.  ... 
doi:10.1109/access.2020.2982196 fatcat:jnya5rscynf3zm7efuucqxafri

Size-Invariant Graph Representations for Graph Classification Extrapolations [article]

Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
2021 arXiv   pre-print
In this work we consider an underexplored area of an otherwise rapidly developing field of graph representation learning: The task of out-of-distribution (OOD) graph classification, where train and test  ...  In general, graph representation learning methods assume that the train and test data come from the same distribution.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.  ... 
arXiv:2103.05045v2 fatcat:yul2sfauanhjfppdvdqz4vanme

A Statistically Efficient and Scalable Method for Exploratory Analysis of High-Dimensional Data

Mohammad S. Rahman, Gholamreza Haffari
2020 SN Computer Science  
Discovering associations among variables is an important data mining task.  ...  The associations can be considered as statistical dependencies among random variables, expressed as the structure of an underlying probabilistic graphical model.  ...  If separator S contains more than one vertex, (S) = 1; otherwise, (S) = 2. The inverse of the covariance matrix is called the precision matrix K = −1 .  ... 
doi:10.1007/s42979-020-0064-2 fatcat:xiat3sx7e5h2jhxb37j2uwhl7q

Partial Difference Operators on Weighted Graphs for Image Processing on Surfaces and Point Clouds

Francois Lozes, Abderrahim Elmoataz, Olivier Lezoray
2014 IEEE Transactions on Image Processing  
Partial difference operators on weighted graphs for image processing on surfaces and point clouds.  ...  In this paper, we propose a simple method to solve such PDEs using the framework of Partial difference Equations (PdEs) on graphs.  ...  Figure (b) shows a point cloud with a selected vertex (in white), and the patch descriptor of that vertex.  ... 
doi:10.1109/tip.2014.2336548 pmid:25020095 fatcat:6webvnpjlnfzxelnj4u7utt5ga

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Roanna Lun, Wenbing Zhao
2015 International journal of pattern recognition and artificial intelligence  
In their study, two different representation schemes, one based on raw multivariate timeseries data, and the other based on the covariance descriptors of the trajectories.  ...  This is accomplished by using an adapted weighting method, which is based on finding the frequencies of patterns.  ... 
doi:10.1142/s0218001415550083 fatcat:7mwojm7lirc3dpyye2zui2kioy
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