Fast Neighborhood Graph Search Using Cartesian Concatenation

Multimedia Data Mining and Analytics ◽

10.1007/978-3-319-14998-1_18 ◽

2015 ◽

pp. 397-417 ◽

Cited By ~ 4

Author(s):

Jingdong Wang ◽

Jing Wang ◽

Gang Zeng ◽

Rui Gan ◽

Shipeng Li ◽

...

Keyword(s):

Graph Search ◽

Neighborhood Graph

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Query-driven iterated neighborhood graph search for large scale indexing

Proceedings of the 20th ACM international conference on Multimedia - MM '12 ◽

10.1145/2393347.2393378 ◽

2012 ◽

Cited By ~ 31

Author(s):

Jingdong Wang ◽

Shipeng Li

Keyword(s):

Large Scale ◽

Graph Search ◽

Neighborhood Graph

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Fast Neighborhood Graph Search Using Cartesian Concatenation

2013 IEEE International Conference on Computer Vision ◽

10.1109/iccv.2013.265 ◽

2013 ◽

Cited By ~ 13

Author(s):

Jing Wang ◽

Jingdong Wang ◽

Gang Zeng ◽

Rui Gan ◽

Shipeng Li ◽

...

Keyword(s):

Graph Search ◽

Neighborhood Graph

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Model Predictive Controller Design for Two-wheeled Vehicle based on Graph Search and Machine Learning

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.133.342 ◽

2013 ◽

Vol 133 (2) ◽

pp. 342-349 ◽

Cited By ~ 1

Author(s):

Daisuke Toishi ◽

Eiji Konaka

Keyword(s):

Machine Learning ◽

Controller Design ◽

Graph Search ◽

Model Predictive Controller ◽

Wheeled Vehicle ◽

Predictive Controller

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New workflow verification algorithm based on graph-search

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00615 ◽

2010 ◽

Vol 30 (3) ◽

pp. 615-616

Author(s):

Zheng-jun DANG ◽

Zhong-jun DU

Keyword(s):

Graph Search ◽

Verification Algorithm ◽

Workflow Verification

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LocaToR: Locating Passive RFID Tags with the Relative Neighborhood Graph

2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing ◽

10.1109/euc.2010.31 ◽

2010 ◽

Cited By ~ 2

Author(s):

Yiyang Zhao ◽

S.C. Cheung ◽

Lionel M. Ni

Keyword(s):

Rfid Tags ◽

Neighborhood Graph ◽

Passive Rfid ◽

Relative Neighborhood Graph

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Discriminant projection method based on optimized neighborhood graph for image feature extraction

2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) ◽

10.1109/icbaie52039.2021.9389993 ◽

2021 ◽

Author(s):

Xiaoheng Tan ◽

Xianfang Li

Keyword(s):

Feature Extraction ◽

Projection Method ◽

Image Feature ◽

Image Feature Extraction ◽

Neighborhood Graph ◽

Discriminant Projection

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Graph Neural Networks for Graph Search

Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) ◽

10.1145/3398682.3399159 ◽

2020 ◽

Author(s):

Yizhou Sun

Keyword(s):

Neural Networks ◽

Graph Search ◽

Graph Neural Networks

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Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

10.1117/12.2250168 ◽

2017 ◽

Author(s):

Enting Gao ◽

Fei Shi ◽

Weifang Zhu ◽

Chao Jin ◽

Min Sun ◽

...

Keyword(s):

Optic Nerve ◽

Optic Nerve Head ◽

Graph Search ◽

Appearance Model ◽

Active Appearance Model ◽

Automated Segmentation ◽

Retinal Layers ◽

Model Based ◽

Active Appearance

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An angle-based neighborhood graph classifier with evidential reasoning

Pattern Recognition Letters ◽

10.1016/j.patrec.2015.11.027 ◽

2016 ◽

Vol 71 ◽

pp. 78-85 ◽

Cited By ~ 7

Author(s):

Yi Yang ◽

De-Qiang Han ◽

Jean Dezert

Keyword(s):

Evidential Reasoning ◽

Neighborhood Graph

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A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.1939 ◽

2013 ◽

Vol 321-324 ◽

pp. 1939-1942

Author(s):

Lei Gu

Keyword(s):

Clustering Algorithm ◽

Experimental Results ◽

Clustering Method ◽

Neighborhood Graph ◽

The Core ◽

Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

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