Performance analysis of fuzzy aggregation operations for combining classifiers for natural textures in images
One objective for classifying pixels belonging to specific textures in natural images is to achieve the best performance in classification as possible. We propose a new unsupervised hybrid classifier. The base classifiers for hybridization are the Fuzzy Clustering and the parametric Bayesian, both s...
| Autores: | , , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2011 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/45466 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/45466 |
| Access Level: | acceso abierto |
| Palavra-chave: | 004 Classifier Combination Fuzzy Aggregation Parametric Estimation Fuzzy Clustering Bayes Classifier Informática (Informática) 1203.17 Informática |
| Resumo: | One objective for classifying pixels belonging to specific textures in natural images is to achieve the best performance in classification as possible. We propose a new unsupervised hybrid classifier. The base classifiers for hybridization are the Fuzzy Clustering and the parametric Bayesian, both supervised and selected by their well-tested performance, as reported in the literature. During the training phase we estimate the parameters of each classifier. During the decision phase we apply fuzzy aggregation operators for making the hybridization. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the final decision with fuzzy aggregation operations, make the main contributions of this paper. |
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