Locality sensitive hash image retrieval parameter optimization method based on empirical fitting

The invention relates to a locality sensitive hash image retrieval parameter optimization method based on empirical fitting, which comprises the following steps of: S1, defining a locality sensitive hash function family H; S2, assuming that k is the number of locality sensitive hash functions and L is the number of hash index tables, when values of L, r and w are determined, calculating a value ofk; S3, taking k functions from the H, and defining a k-dimensional locality sensitive ash function family G; and S4, taking L hash functions from the G, and establishing L hash index tables. According to the invention, a locality sensitive hash image retrieval parameter optimization empirical formula is obtained by a regression analysis method, and by using the empirical formula, calculation steps can be effectively reduced, complexity of parameter optimization of an algorithm can be reduced, and operation efficiency of the algorithm can be improved. Meanwhile, the locality sensitive hash image retrieval parameter op