Scale‐free network model for template matching based on kernel histogram

2014 ◽  
Vol 50 (9) ◽  
pp. 669-671 ◽  
Author(s):  
Ri‐Sheng Han ◽  
Shi‐Gen Shen ◽  
Guang‐Xue Yue ◽  
Hui Ding
Keyword(s):  
Network Model ◽  
Template Matching ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals A Novel BA Complex Network Model on Color Template Matching

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Risheng Han ◽  
Shigen Shen ◽  
Guangxue Yue ◽  
Hui Ding
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Template Matching ◽  
Preferential Attachment ◽  
Color Space ◽  
Scale Free Network ◽  
Color Distribution ◽  
Scale Free ◽  
Matching Process ◽  
Free Network

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template’s color distribution. And then the template’s BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

The Barabási and Albert scale-free network model

2018 ◽  
Vol 35 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Lei Zhu ◽  
Lei Wang ◽  
Xiang Zheng ◽  
Yuzhang Xu
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Scale-Free Networks Based on the Value of Interest

2013 ◽  
Vol 753-755 ◽  
pp. 2959-2962
Author(s):  
Jun Tao Yang ◽  
Hui Wen Deng
Keyword(s):  
Network Model ◽  
Power Law ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Scale Free ◽  
Distribution Property ◽  
Scale Free Networks ◽  
Free Model ◽  
Free Network ◽  
Interest Model

Assigning the value of interest to each node in the network, we give a scale-free network model. The value of interest is related to the fitness and the degree of the node. Experimental results show that the interest model not only has the characteristics of the BA scale-free model but also has the characteristics of fitness model, and the network has a power-law distribution property.

scholarly journals Geography in a scale-free network model

2002 ◽  
Vol 66 (5) ◽  
Author(s):  
C. P. Warren ◽  
L. M. Sander ◽  
I. M. Sokolov
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

scholarly journals Analytic solution of a static scale-free network model

2005 ◽  
Vol 44 (2) ◽  
pp. 241-248 ◽  
Author(s):  
M. Catanzaro ◽  
R. Pastor-Satorras
Keyword(s):  
Network Model ◽  
Analytic Solution ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

A novel scale-free network model based on clique growth

2009 ◽  
Vol 16 (3) ◽  
pp. 474-477 ◽  
Author(s):  
Bo Wang ◽  
Xu-hua Yang ◽  
Wan-liang Wang
Keyword(s):  
Network Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Model Based ◽  
Free Network

THE RELEVANCE-STRENGTH IN A SCALE-FREE NETWORK

2008 ◽  
Vol 22 (31) ◽  
pp. 3053-3059 ◽  
Author(s):  
HYUN-JOO KIM
Keyword(s):  
Network Model ◽  
Shortest Path ◽  
Power Law ◽  
Path Length ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network ◽  
The Mean

We introduce a new quantity, relevance-strength which describes the relevance of a node to the others in a scale-free network. We define a weight between two nodes i and j based on the shortest path length between them and the relevance-strength of a node is defined as the sum of the weights between it and others. For the Barabási and Albert model which is a well-known scale-free network model, we measure the relevance-strength of each node and study the correlations with other quantities, such as the degree, the mean degree of neighbors of a node, and the mean relevance-strength of neighbors. We find that the relevance-strength shows power law behaviors and the crossover behaviors for the degree and the mean relevance-strength of neighbors. Also, we study the scaling behaviors of the relevance-strength for various average relevance-strength for all nodes.

scholarly journals Evolution of egalitarian punishment

2011 ◽  
Vol 2 (2) ◽  
pp. 20-23 ◽  
Author(s):  
Kohei Tamura ◽  
Rieko C Morita ◽  
Yasuo Ihara
Keyword(s):  
Network Model ◽  
Population Model ◽  
Computational Experiments ◽  
Evolution Of Cooperation ◽  
Scale Free Network ◽  
Social Equality ◽  
Scale Free ◽  
Altruistic Punishment ◽  
Individual Based Models ◽  
Free Network

Punishment has been deemed as a key to solve the puzzle of the evolution of cooperation. Recent studies have suggested that altruistic punishment may be motivated by preference for social equality (egalitarian punishment). Here we construct individual-based models to investigate the effectiveness of egalitarian punishment in promoting cooperation. Based on computational experiments, we first show that egalitarian punishment is as effective as classic punishment, which directly observes others' strategies, in a meta-population model. We then use a scale-free network model to show that egalitarian punishment can be effective even when heterogeneity in the number of interactions among individuals is incorporated. Finally, we show that generosity in punishment can affect co-evolution of egalitarian punishment and cooperation.

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