A framework that constructs an adaptive region similarity network from an initial segmentation using color and texture features and applies community detection algorithms to produce the final image segmentation, with tests on the Berkeley dataset showing improved performance.
On Accuracy of Community Structure Discovery Algorithms
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abstract
Community structure discovery in complex networks is a quite challenging problem spanning many applications in various disciplines such as biology, social network and physics. Emerging from various approaches numerous algorithms have been proposed to tackle this problem. Nevertheless little attention has been devoted to compare their efficiency on realistic simulated data. To better understand their relative performances, we evaluate systematically eleven algorithms covering the main approaches. The Normalized Mutual Information (NMI) measure is used to assess the quality of the discovered community structure from controlled artificial networks with realistic topological properties. Results show that along with the network size, the average proportion of intra-community to inter-community links is the most influential parameter on performances. Overall, "Infomap" is the leading algorithm, followed by "Walktrap", "SpinGlass" and "Louvain" which also achieve good consistency.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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A General Framework for Complex Network-Based Image Segmentation
A framework that constructs an adaptive region similarity network from an initial segmentation using color and texture features and applies community detection algorithms to produce the final image segmentation, with tests on the Berkeley dataset showing improved performance.