If you're interested in this article, then you may interested in locality sensitive hashing (LSH), a randomized hash that has been used seemingly everywhere. I recently used it to speed up music source separation (papers pending).
The idea is similar to the one mentioned in this article, but more general. Unlike a cryptographically secure hash where x != y implies that h(x) != h(y) (collisions aside), LSH says that if x and y are "near", then P(h(x) = h(y)) is "high". This quality is important when doing robust similarity search. For example, if your image is noisy or rotated or scaled, you hope that you can still find the clean version in a database.
LSH has been used in many application domains including images, video, music, text, bioinformatics, and more. LSH is not directly comparable to a feature extraction algorithm such as SIFT.
The idea is similar to the one mentioned in this article, but more general. Unlike a cryptographically secure hash where x != y implies that h(x) != h(y) (collisions aside), LSH says that if x and y are "near", then P(h(x) = h(y)) is "high". This quality is important when doing robust similarity search. For example, if your image is noisy or rotated or scaled, you hope that you can still find the clean version in a database.
LSH has been used in many application domains including images, video, music, text, bioinformatics, and more. LSH is not directly comparable to a feature extraction algorithm such as SIFT.
[Edited for clarity.]