scholarly journals Convex Relaxation Methods for Community Detection

2021 ◽  
Vol 36 (1) ◽  
pp. 2-15
Author(s):  
Xiaodong Li ◽  
Yudong Chen ◽  
Jiaming Xu
Keyword(s):  
Community Detection ◽  
Convex Relaxation ◽  
Relaxation Methods

scholarly journals Stabilised bias field: segmentation with intensity inhomogeneity

2016 ◽  
Vol 10 (4) ◽  
pp. 302-313 ◽  
Author(s):  
Jack Spencer ◽  
Ke Chen
Keyword(s):  
Convex Relaxation ◽  
Intensity Variation ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Variational Model ◽  
Great Success ◽  
Two Phase ◽  
Relaxation Methods ◽  
Piecewise Constant ◽  
Field Estimation

Automatic segmentation in the variational framework is a challenging task within the field of imaging sciences. Achieving robustness is a major problem, particularly for images with high levels of intensity inhomogeneity. The two-phase piecewise-constant case of the Mumford-Shah formulation is most suitable for images with simple and homogeneous features where the intensity variation is limited. However, it has been applied to many different types of synthetic and real images after some adjustments to the formulation. Recent work has incorporated bias field estimation to allow for intensity inhomogeneity, with great success in terms of segmentation quality. However, the framework and assumptions involved lead to inconsistencies in the method that can adversely affect results. In this paper we address the task of generalising the piecewise-constant formulation, to approximate minimisers of the original Mumford-Shah formulation. We first review existing methods for treating inhomogeneity, and demonstrate the inconsistencies with the bias field estimation framework. We propose a modified variational model to account for these problems by introducing an additional constraint, and detail how the exact minimiser can be approximated in the context of this new formulation. We extend this concept to selective segmentation with the introduction of a distance selection term. These models are minimised with convex relaxation methods, where the global minimiser can be found for a fixed fitting term. Finally, we present numerical results that demonstrate an improvement to existing methods in terms of reliability and parameter dependence, and results for selective segmentation in the case of intensity inhomogeneity.

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

2016 ◽  
Author(s):  
Ananth Kalyanaraman ◽  
Mahantesh Halappanavar ◽  
Daniel Chavarría-Miranda ◽  
Hao Lu ◽  
Karthi Duraisamy
Keyword(s):  
Community Detection
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