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

Muhammad Uzair, Faisal Shafait, Bernard Ghanem, Ajmal Mian

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1211-1223
Number of pages13
JournalNeural Computing and Applications
Issue number4
StatePublished - Dec 9 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Australian Research Council (ARC) Grant DP110102399 and UWA Research Collaboration Award 2014.


Dive into the research topics of 'Representation learning with deep extreme learning machines for efficient image set classification'. Together they form a unique fingerprint.

Cite this