1. bookVolumen 72 (2021): Edición 6 (December 2021)
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Formato
Revista
eISSN
1339-309X
Primera edición
07 Jun 2011
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6 veces al año
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access type Acceso abierto

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

Publicado en línea: 22 Dec 2021
Volumen & Edición: Volumen 72 (2021) - Edición 6 (December 2021)
Páginas: 374 - 380
Recibido: 03 May 2021
Detalles de la revista
License
Formato
Revista
eISSN
1339-309X
Primera edición
07 Jun 2011
Calendario de la edición
6 veces al año
Idiomas
Inglés
Abstract

Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

Keywords

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