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Implicit Sparse Code Hashing
[article]
2015
arXiv
pre-print
We address the problem of converting large-scale high-dimensional image data into binary codes so that approximate nearest-neighbor search over them can be efficiently performed. Different from most of the existing unsupervised approaches for yielding binary codes, our method is based on a dimensionality-reduction criterion that its resulting mapping is designed to preserve the image relationships entailed by the inner products of sparse codes, rather than those implied by the Euclidean
arXiv:1512.00130v1
fatcat:3i3wqbb225dqdjl4iugywjpika