Cross-media retrieval method based on local sensitive hash algorithm and neural network

Abstract

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.

Description

Technical field [0001] The invention relates to the technical field of cross-media retrieval, in particular to a cross-media retrieval method based on a local sensitive hash algorithm and a neural network. Background technique [0002] In the era of cross-media big data, the massive multi-modal information generated all the time has brought huge cross-media retrieval needs, such as using text to search for images or videos, and vice versa. For example, an entry on Wikipedia usually contains text descriptions and example images. The retrieval of this information requires the construction of cross-media indexes and learning methods. Compared with traditional single media retrieval, the core problem of cross-media retrieval is how to mine the associations between the same or related semantic objects represented by different media. [0003] At present, many solutions have been proposed for the core problem of cross-media retrieval in the world. Existing cross-media retrieval methods ...

AI Technical Summary

Problems solved by technology

[0014] The existing cross-media retrieval methods all have the same technical defect, that is, only considering the cross-media retrieval method itself and ignoring some feasible optimization processing of the document set, because there are a large number of irrelevant documents in the document set, so Preprocessing the document set before precise query, increasing the proportion of relevant documents in the document set is of great significance for improving retrieval efficiency

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