Minimal Loss Hashing for Compact Binary Codes
Version 1.1 - Updated on Aug 10, 2012.
This is an implementation of minimal loss hashing (MLH) method  for learning similarity preserving hash functions that map high-dimensional data onto binary codes. Binary codes received lots of recent interest because they are storage efficient and they facilitate fast retrieval.
The code is implemented in Matlab, with a few helper functions implemented in C++ for efficiency reasons. Please refer to the README file for instructions on usage and compilation. The code is ready for re-running experiments described in the paper on Euclidean and semantic 22K LabelMe, and on 6 other datasets (10D uniform, mnist, LabelMe, notredame, peekaboom, nursery). Please download the datasets separately from 22K LabelMe (courtesy of Rob Fergus) and 5 other datasets (courtesy of Brian Kulis).