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Neural Computing and Applications

Erschienen in:

09.12.2016 | Original Article

Representation learning with deep extreme learning machines for efficient image set classification

verfasst von: Muhammad Uzair, Faisal Shafait, Bernard Ghanem, Ajmal Mian

Erschienen in: Neural Computing and Applications | Ausgabe 4/2018

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Abstract

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

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Titel: Representation learning with deep extreme learning machines for efficient image set classification
verfasst von: Muhammad Uzair
Faisal Shafait
Bernard Ghanem
Ajmal Mian
Publikationsdatum: 09.12.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2758-x

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