Fast computation of min-Hash signatures for image collections
A new method for highly efficient min-Hash generation for document collections is proposed. It exploits the inverted file structure which is available in many applications based on a bag or a set of words. Fast min-Hash generation is important in applications such as image clustering where good recall and precision requires a large number of min-Hash signatures. Using the set of words represenation, the novel exact min-Hash generation algorithm achieves approximately a 50-fold speed-up on two dataset with 105 and 106 images respectively. We also propose an approximate min-Hash assignment process which reaches a more than 200-fold speed-up at the cost of missing about 2-3% of matches. We also experimentally show that the method generalizes to other modalities with significantly different statistics.