The invention discloses a cross-media retrieval method based on local sensitive hash
algorithm and neural network, and relates to the technical field of cross-media retrieval. The method comprises two stages of local sensitive hash and
hash function learning, wherein in the stage of local sensitive hash, the image data is mapped to hash buckets in m hash tables G = [g1, g2,...,gm] (which is an element of a set R<k*m>) by the local sensitive hash
algorithm, wherein G is the set of m hash tables, gj is the jth
hash table, and k is the length of the hash code corresponding to the hash bucket; and in the stage of
hash function learning, the text data is respectively mapped to hash functions Ht = (Ht (1), Ht (2), ..., Ht (m), Ht (j)) in corresponding hash buckets in m hash tables by the neural network
algorithm learning, wherein Ht (j), (1<=j<=m) represents the learned
hash function Ht corresponding to the jth
hash table. After getting the functions of these two phases, all the images and the documents are further coded and indexed for more accurate retrieval.