Sparse Kernel Learning for Image Set Classification

  • Muhammad UzairEmail author
  • Arif Mahmood
  • Ajmal Mian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


No single universal image set representation can efficiently encode all types of image set variations. In the absence of expensive validation data, automatically ranking representations with respect to performance is a challenging task. We propose a sparse kernel learning algorithm for automatic selection and integration of the most discriminative subset of kernels derived from different image set representations. By optimizing a sparse linear discriminant analysis criterion, we learn a unified kernel from the linear combination of the best kernels only. Kernel discriminant analysis is then performed on the unified kernel. Experiments on four standard datasets show that the proposed algorithm outperforms current state-of-the-art image set classification and kernel learning algorithms.


Linear Discriminant Analysis Multiple Kernel Learn Unify Kernel Sparse Linear Combination Subspace Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research work was supported by ARC grants DP1096801 and DP110102399.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science and Software EngineeringThe University of Western AustraliaCrawleyAustralia

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