A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf
.
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
In computer vision there has been increasing interest in learning hashing codes whose Hamming distance approximates the data similarity. The hashing functions play roles in both quantizing the vector space and generating similarity-preserving codes. Most existing hashing methods use hyper-planes (or kernelized hyper-planes) to quantize and encode. In this paper, we present a hashing method adopting the k-means quantization. We propose a novel Affinity-Preserving K-means algorithm which
doi:10.1109/cvpr.2013.378
dblp:conf/cvpr/HeWS13
fatcat:apavshm5mfbtpceug2ffxpif7m