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A hierarchical graph-based markovian clustering approach for the unsupervised segmentation of textured color images

8 years 7 months ago
A hierarchical graph-based markovian clustering approach for the unsupervised segmentation of textured color images
In this paper, a new unsupervised hierarchical approach to textured color images segmentation is proposed. To this end, we have designed a two-step procedure based on a grey-scale Markovian oversegmentation step, followed by a Markovian graph-based clustering algorithm, using a decreasing merging threshold schedule, which aims at progressively merging neighboring regions with similar textural features. This Hierarchical segmentation method, using two levels of representation, has been successfully applied on the Berkeley Segmentation Dataset and Benchmark (BSDB[1]). The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature. Key words : Hierarchical Markovian segmentation, textural segmentation, graph partitioning, regions merging, image Berkeley database.
Rachid Hedjam, Max Mignotte
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICIP
Authors Rachid Hedjam, Max Mignotte
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