Funções de agregação baseadas em integral de Choquet aplicadas em redimensionalização de imagens
Fecha
2019Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
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10.5335/rbca.v11i1.9082
Resumen
The increasing data volume, coupled with the high complexity of these data, has generated the need to develop increasingly efficient knowledge extraction techniques, both in computational cost and precision. Most of the problems that are addressed by these techniques have complex information to be identified. For this, machine learning methods are used, where these methods use a variety of functi ...
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The increasing data volume, coupled with the high complexity of these data, has generated the need to develop increasingly efficient knowledge extraction techniques, both in computational cost and precision. Most of the problems that are addressed by these techniques have complex information to be identified. For this, machine learning methods are used, where these methods use a variety of functions inside the different steps that are employed in their architectures. One of these consists in the use of aggregation functions to resize images. In this context, a study of aggregation functions based on the Choquet integral is presented, where the main feature of Choquet integral, in comparison with other aggregation functions, resides in the fact that it considers, through the fuzzy measure, the interaction between the elements to be aggregated. Thus, an evaluation study of the performance of the standard Choquet integral functions is presented (Choquet integral based on Copula in relation to the maximum and average functions) looking for results that may be better than the usual applied aggregation functions. The results of such comparisons are promising when evaluated through measures of image quality. [--]
Materias
Aggregation functions,
Choquet integral,
Image processing
Editor
Universidade Passo Fundo
Publicado en
Revista Brasileira de Computação Aplicada (2019), v. 11, n. 1, pp. 80-87
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila
Versión del editor
Entidades Financiadoras
As autoras Camila Alves Dias e Jéssica C. Saldivia Bueno agradecem à CAPES pelo apoio financeiro recebido. Graçaliz P. Dimuro tem financiamento do CNPq (processo 305882/2016-3).
Eduardo N. Borges tem financiamento da FAPERGS (TO 17/2551-0000872-3).