VLFeat - a computer vision toolbox

The VLFeat open source computer vision library that implements popular

• feature extraction algorithms (such as SIFT, MSER, and quick shift),
  
• clustering algorithms (such as integer k-means, hierarchical k-means, and agglomerative information bottleneck), and
  
• matching algorithms (such as randomized kd-trees).
  

It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout.

Bloom filters and Locality Sensitive Hashing

Locality Sensitive Hashing (LSH) of l-bits is achieved by carrying out l independent random cuts of the Euclidean space: if two data points are in the same side of all these cuts, they are very likely to be nearest neighbours. In this sense, I think Bloom filters (that also relies on a number of independent hashing functions) can be conceptually considered as the extreme case of LSH: each of its cuts tries to separate one data point from the rest.