Representation learning with deep extreme learning machines for efficient image set classification
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
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AbstractEfficient 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.
CitationUzair M, Shafait F, Ghanem B, Mian A (2016) Representation learning with deep extreme learning machines for efficient image set classification. Neural Computing and Applications. Available: http://dx.doi.org/10.1007/s00521-016-2758-x.
SponsorsThis work was supported by the Australian Research Council (ARC) Grant DP110102399 and UWA Research Collaboration Award 2014.