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• W2295680981 abstract "In this paper, we explore the image segmentation task using a graph clustering approach. We formulate this clustering as a diffusion scheme whose steady state is determined by the Random Walker (RW) method. Then, we discover the equivalence of this diffusion with the Susceptible — Infected — Recovered (SIR) model, a well-studied epidemic propagation model. We further argue that using a Region Adjacency Graph (RAG) exploits the clustering properties and leads to a dimensionality reduction. Finally, we propose a novel method called Normalized Random Walker (NRW) algorithm which extends the RW method. Qualitative and quantitative experiments validate the efficiency and robustness of our method, with respect to parameter tuning, seed quality and location." @default.
• W2295680981 created "2016-06-24" @default.
• W2295680981 creator A5036706118 @default.
• W2295680981 creator A5079243420 @default.
• W2295680981 date "2015-09-01" @default.
• W2295680981 modified "2023-09-26" @default.
• W2295680981 title "Unifying the random walker algorithm and the SIR model for graph clustering and image segmentation" @default.
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• W2295680981 doi "https://doi.org/10.1109/icip.2015.7351205" @default.
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