Evaluation of LBP and HOG descriptors for clothing attribute description
In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been i...
| Autores: | , , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2014 |
| País: | España |
| Repositorio: | accedaCRIS portal de investigación de la Universidad de las Palmas de Gran Canaria |
| OAI Identifier: | oai:accedacris.ulpgc.es:10553/15753 |
| Acesso em linha: | http://hdl.handle.net/10553/15753 |
| Access Level: | acceso abierto |
| Palavra-chave: | 120304 Inteligencia artificial LBP HOG Clothing description |
| Resumo: | In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75% in most cases, reaching 80% for the necktie or sleeve length attributes. |
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