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

16 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)1211-1223
Number of pages13
JournalNeural Computing and Applications
Volume30
Issue number4
DOIs
StatePublished - Dec 9 2016

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