Diversified Fisher kernel: encoding discrimination in Fisher features to compete deep neural models for visual classification task

Fisher kernels derived from stochastic probabilistic models such as restricted and deep Boltzmann machines have shown competitive visual classification results in comparison to widely popular deep discriminative models. This genre of Fisher kernels bridges the gap between shallow and deep learning p...

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Detalhes bibliográficos

Principais autores:	Sarah Ahmed, Tayyaba Azim
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Wiley 2020-12-01
coleção:	IET Computer Vision
Assuntos:	large‐scale visual retrieval feature selection technique discriminative feature representations dimensional Fisher features kernel learning classification performance
Acesso em linha:	https://doi.org/10.1049/iet-cvi.2019.0208
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