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Representation Learning with Deep Extreme Learning Machines for Efficient Image Set Classification
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<span title="2015-04-01">2015</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2015-03-09">2015</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.02445v1">arXiv:1503.02445v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynjaqsgsj5dezbmvrk5v52oef4">fatcat:ynjaqsgsj5dezbmvrk5v52oef4</a> </span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.02445v1">arXiv:1503.02445v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynjaqsgsj5dezbmvrk5v52oef4">fatcat:ynjaqsgsj5dezbmvrk5v52oef4</a> </span>
<span title="2015-03-16">2015</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1503.02445v2">arXiv:1503.02445v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mwst6wpuq5ca5lkbyqjbu35wna">fatcat:mwst6wpuq5ca5lkbyqjbu35wna</a> </span>
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Efficient and accurate joint 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 non-linear structure
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... f image sets with Deep Extreme Learning Machines (DELM) 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 (Honda/UCSD, CMU Mobo, YouTube Celebrities, Celebrity-1000, ETH-80) 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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