汉明嵌入

汉明嵌入简介：http://blog.csdn.net/definewl/article/details/41217441

汉明嵌入：http://blog.csdn.net/yylgg0922/article/details/47284879

汉明距离：http://blog.sina.com.cn/s/blog_663457ed01017bry.html

合集： http://github.com/vd001/Dancing-around-hamming-distance

Dancing-around-hamming-distance

Collection of various code snippets using hamming distance for image retrival.

1. http://www.cs.toronto.edu/~norouzi/research/mlh/ Minimal Loss Hashing for Compact Binary Codes.
2. http://www.cs.toronto.edu/~norouzi/research/mih/ Fast Exact Search in Hamming Space with Multi-Index Hashing.
3. https://github.com/kevinlin311tw/caffe-cvprw15 Deep Learning of Binary Hash Codes.
4. http://www.cs.huji.ac.il/~yweiss/SpectralHashing/ Spectral Hashing.

Datases:

1. http://groups.csail.mit.edu/vision/TinyImages/
2. http://www.cs.toronto.edu/~kriz/cifar.html
3. http://www.vision.caltech.edu/Image_Datasets/Caltech101/
4. http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/

tags: hamming distance, hamming embedding, binary codes, locality sensitive hashing, semantic hashing, min-hash.

Efficient hamming distance computation:
HammingDistance(x,y) = HammingWeight(x^y)
https://en.wikipedia.org/wiki/Hamming_weight#Efficient_implementation
http://notabs.org/blcutil/
http://stackoverflow.com/questions/12380478/bits-counting-algorithm-brian-kernighan-in-an-integer-time-complexity
https://web.archive.org/web/20100604050922/http://infolab.stanford.edu/~manku/bitcount/bitcount.c
http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetKernighan
https://github.com/mariusmuja/flann/search?utf8=%E2%9C%93&q=hamming
https://github.com/mariusmuja/flann/blob/6f1d8f808f2605488c2882ce8fcfa3d5569576bc/doc/references.bib
http://cage.ugent.be/~klein/papers/popc-article.pdf

https://github.com/mrgloom/metric-tree-demo

TODO:

1. Semantic hashing.
    https://github.com/gynnash/AutoEncoder
    http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html 
    http://www.cs.toronto.edu/~fritz/absps/sh.pdf
    http://www.cs.toronto.edu/~amnih/cifar/talks/salakhut_talk.pdf
2. Locality sensitive hashing with hamming distance.
    http://stackoverflow.com/questions/12952729/how-to-understand-locality-sensitive-hashing

Deep learning:
https://github.com/BVLC/caffe/wiki/Model-Zoo#deep-learning-of-binary-hash-codes-for-fast-image-retrieval
https://github.com/kevinlin311tw/Caffe-DeepBinaryCode
https://github.com/kevinlin311tw/caffe-cvprw15
https://www.cs.toronto.edu/~hinton/csc2535/notes/lec8b.pdf
http://arxiv.org/pdf/1404.1777v2.pdf
https://github.com/kevinlin311tw/Caffe-DeepBinaryCode

Binary features (BRIEF, ORB, BRISK):
http://www.cs.ubc.ca/~lowe/papers/12mujaCRV.pdf 
http://infoscience.epfl.ch/record/126376/files/OzuysalFL07.pdf

http://frahm.web.unc.edu/files/2014/01/Fast-Organization-of-Large-Photo-Collections.pdf
http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gong_Web_Scale_Photo_2015_CVPR_paper.pdf

Presentations:
too complicated math, some relations to spectral hashing.
https://www.robots.ox.ac.uk/~vgg/rg/slides/binarycodes.pdf
https://www.robots.ox.ac.uk/~vgg/rg/

Cosine distance:
Approaches different from hamming distance - "Randomly project points to low dimensional bit signatures such that cosine distance is approximately preserved". 

Perceptive hash:
https://github.com/JohannesBuchner/imagehash