Classification of Paintings by Artistic Style Using Color and Texture Features
Título del documento: Classification of Paintings by Artistic Style Using Color and Texture Features
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560761
ISSN: 1405-5546
Autores: 1
2
2
2
3
Instituciones: 1Universidad de Guanajuato, Departamento de Estudios Multidisciplinarios, México
2Universidad de Guanajuato, Departamento de Arte y Empresa, Salamanca. México
3Universidad de Guanajuato, División de Ingenierías, Salamanca. México
Año:
Periodo: Oct-Dic
Volumen: 26
Número: 4
Paginación: 1503-1514
País: México
Idioma: Inglés
Resumen en inglés In this paper, an approach for the classification of paintings by artistic style using color and texture features is proposed. Our approach automatically extracts a set of visual features that effectively discriminate among diverse artistic styles. Additionally, our proposal performs an effective selection of the most relevant features to be used in an artificial neural network architecture. Using the most important features allows our system to achieve an efficient learning process. The proposed system analyzes digitized paintings using a combination of color and texture features, which have shown to be highly discriminatory. Our approach consists of two main stages: training and testing. Firstly, in the training stage, the features from seven artistic styles are extracted to train a multi-layer perceptron. Secondly, the learned model is utilized to determine the artistic style of a given incoming painting to our system. The experimental results, on an extensive dataset of digitized paintings, show that our method obtains a higher accuracy in comparison with those obtained by the state-of-the-art methods. Moreover, our proposal attains a higher accuracy rate using fewer features descriptors.
Keyword: ANN,
PCA,
Artistic style,
Classification,
Color features,
Paintings,
Texture features
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