Permutation grouping: intelligent Hash function design for audio & image retrieval

The combination of MinHash-based signatures and locality- sensitive hashing (LSH) schemes has been effectively used for finding approximate matches in very large audio and image retrieval systems. In this study, we introduce the idea of permutation-grouping to intelligently design the hash functions that are used to index the LSH tables. This helps to overcome the inefficiencies introduced by hashing real-world data that is noisy, structured, and most importantly is not independently and identically distributed. Through extensive tests, we find that permutation-grouping dramatically increases the efficiency of the overall retrieval system by lowering the number of low-probability candidates that must be examined by 30-50%.