http://www.cnr.it/ontology/cnr/individuo/prodotto/ID160887
MUSCLE NoE - DN6.1 - Computational learning methods for unsupervised segmentation (Rapporti progetti di ricerca)
- Type
- Label
- MUSCLE NoE - DN6.1 - Computational learning methods for unsupervised segmentation (Rapporti progetti di ricerca) (literal)
- Anno
- 2007-01-01T00:00:00+01:00 (literal)
- Alternative label
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- Salerno E.; Wilson S.; Bedini L.; Kuruoglu E. E.; Tonazzini A.; Vatsa R. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- Project title: Multimedia Understanding through Semantics, Computation and Learning (MUSCLE NoE) FP6 507752. Project report DN6.1, 2007. (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
- ABSTRACT: We summarize the motivation and scope of our session on Computational Learning Methods for Unsupervised Segmentation (CLeMUS) at the KES 2007 Conference, and review the relationships between our Bayesian Component Separation view on unsupervised segmentation and recent accounts on soft segmentation based on statistical-probabilistic and/or variational approaches. Soft segmentation is more general than the classical partition-based approach to segmentation (hard segmentation), and its output can be reduced to a hard domain partition by applying some decision rule (e.g., thresholding or maximum likelihood). The papers presented at CLeMUS are reviewed and related to one another and to common concepts taken from recent literature. (literal)
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- Simon Wilson and Ritcha Vatsa are with the Department of Statistics, Trinity College, Dublin (literal)
- Titolo
- MUSCLE NoE - DN6.1 - Computational learning methods for unsupervised segmentation (literal)
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