Video Similarity Measurement Based on Attributed Relational Graph Matching

3D CAD model retrieval has received a lot of attentions in the academic community. Most existent methods for 3D model similarity assessment focus on component models instead of assembly models. In this paper, an assembly model similarity assessment method is presented in order to find the similar assemblies model for design reuse. First, assembly model is described as component attributed relational graph. Then, the compatibility matrix between two assemblies is calculated, which serves as the measure of their similarity. Finally, the optimal matching under the measures is calculated using Hungarian Method. Experimental results show that this method is able to support the assembly similarity evaluation.

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Attributed relational graph matching based on the nested assignment structure

Pattern Recognition ◽

10.1016/j.patcog.2009.09.012 ◽

2010 ◽

Vol 43 (3) ◽

pp. 914-928 ◽

Cited By ~ 13

Author(s):

Duck Hoon Kim ◽

Il Dong Yun ◽

Sang Uk Lee

Keyword(s):

Graph Matching ◽

Attributed Relational Graph

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Online hand-sketched graphics recognition based on attributed relational graph matching

Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393) ◽

10.1109/wcica.2000.862507 ◽

2002 ◽

Cited By ~ 5

Author(s):

Li Changhua ◽

Yang Bing ◽

Xie Weixin

Keyword(s):

Graph Matching ◽

Graphics Recognition ◽

Attributed Relational Graph

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Automatic Online Educational Game Content Creation by Identifying Similar Chinese Characters with Radical Extraction and Graph Matching Algorithms

International Journal of Distance Education Technologies ◽

10.4018/jdet.2010070103 ◽

2010 ◽

Vol 8 (3) ◽

pp. 31-46 ◽

Cited By ~ 1

Author(s):

Kwong-Hung Lai ◽

Howard Leung ◽

Zhi-Hui Hu ◽

Jeff K.T. Tang ◽

Yun Xu

Keyword(s):

Graph Matching ◽

Educational Games ◽

Chinese Language ◽

Spatial Relationship ◽

Educational Game ◽

Chinese Characters ◽

Matching Method ◽

Matching Algorithm ◽

Content Creation ◽

Attributed Relational Graph

One of the difficulties in learning Chinese characters is distinguishing similar characters. This can cause misunderstanding and miscommunication in daily life. Thus, it is important for students learning the Chinese language to be able to distinguish similar characters and understand their proper usage. In this paper, the authors propose a game style framework to train students to distinguish similar characters. A major component in this framework is the search for similar Chinese characters in the system. From the authors’ prior work, they find the similar characters by the radical information and stroke correspondence determination. This paper improves the stroke correspondence determination by using the attributed relational graph (ARG) matching algorithm that considers both the stroke and spatial relationship during matching. The experimental results show that the new proposed method is more accurate in finding similar Chinese characters. Additionally, the authors have implemented online educational games to train students to distinguish similar Chinese characters and made use of the improved matching method for creating the game content automatically.

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Attributed relational graph matching by neural-gas networks

Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501) ◽

10.1109/nnsp.2000.889428 ◽

2002 ◽

Cited By ~ 2

Author(s):

P.N. Suganthan

Keyword(s):

Graph Matching ◽

Gas Networks ◽

Neural Gas ◽

Attributed Relational Graph

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The Node-to-Node Graph Matching Algorithm Schema

Graph-Based Methods in Computer Vision ◽

10.4018/978-1-4666-1891-6.ch003 ◽

2012 ◽

pp. 58-70

Author(s):

Guoxing Zhao ◽

Jixin Ma

Keyword(s):

Graph Matching ◽

Similarity Measurement ◽

Matching Algorithm ◽

Node Similarity ◽

High Level

Many graph matching algorithms follow the approach of node-similarity measurement, that is, matching graphs by means of comparing the corresponding pairs of nodes of the graphs. Based on this idea, the authors propose a high-level schema for node-to-node graph matching, namely N2N graph matching algorithm schema. The chapter shows that such a N2N graph matching algorithm schema is versatile enough to subsume most of the representative node-to-node based graph matching algorithms. It is also shown that improved algorithms can be derived from this N2N graph matching schema, compared with various corresponding algorithms. In addition, the authors point out the limitation and constraints of the propose algorithm schema and suggest some possible treatments.

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