An Oil Painters Recognition Method Based on Cluster Multiple Kernel Learning Algorithm
Author
dc.contributor.author
Liao, Zhifang
Author
dc.contributor.author
Gao, Le
Author
dc.contributor.author
Zhou, Tian
Author
dc.contributor.author
Fan, Xiaoping
Author
dc.contributor.author
Zhang, Yan
Author
dc.contributor.author
Wu, Jinsong
Admission date
dc.date.accessioned
2019-10-15T12:26:11Z
Available date
dc.date.available
2019-10-15T12:26:11Z
Publication date
dc.date.issued
2019
Cita de ítem
dc.identifier.citation
IEEE Access, Volumen 7,
Identifier
dc.identifier.issn
21693536
Identifier
dc.identifier.other
10.1109/ACCESS.2019.2899389
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/171764
Abstract
dc.description.abstract
A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly.
Lenguage
dc.language.iso
en
Publisher
dc.publisher
Institute of Electrical and Electronics Engineers Inc.