Aiming at the problem that low retrieval precision when using random sampling to calculate weight results in inaccurate weight assignment

a Hash code weighted ranking image retrieval algorithm from coarse to fine is proposed to solve the problem. A shorter Hash code is generated to improve coding efficiency. Weight value of each Hash bit is calculated by the sampling subset obtained by using data dependence difference

and a candidate nearest neighbor set is obtained according to the weighted Hamming distance. Ranking score of the data is calculated in the set

the retrieval precision is further improved by reordering according to the score

and the nearest neighbor search of the query image is realized. The result of the emulation experiment shows that average precision of the proposed algorithm is improved by 13. 33 % and 11. 61 % respectively

when the coding length are48 bit and 96 bit on MNIST database.