Method and device for retrieving similarity of picture messages

Abstract

The invention discloses a method and a device for retrieving the similarity of pictures and belongs to the field of the graphic images. The method comprises the steps of obtaining the to-be-retrieved picture characteristics of to-be-retrieved pictures, hashing the to-be-retrieved picture characteristics by use of an LSH (Locality Sensitive Hashing) algorithm to generate hash values of the to-be-retrieved pictures, finding database picture hash values of which are matched and similar to the hash values of the to-be-retrieved pictures om a hash table corresponding to every hash values of the to-be-retrieved pictures, finding database pictures according to the database picture hash values, and according to Euclidean distances of the characteristics of the database pictures and the characteristics of the to-be-retrieved pictures, selecting a preset number of database pictures from the database pictures having relatively smaller Euclidean distances. The method and the device have the characteristics that the problem that a user cannot retrieve the most similar pictures from a plurality of results obtained by the LSH algorithm at the present is solved, and the advantages of LSH in reducing the time-space complexity in picture similarity retrieval and supporting high-dimensional data retrieval are better developed by virtue of the combination of the LSH algorithm and a linear retrieval algorithm.

Description

technical field [0001] The invention relates to the field of graphics and images, in particular to a method and device for similarity retrieval of pictures. Background technique [0002] With the rapid development of network communication technology, the amount of data in the network is increasing, and the difficulty of retrieving similar information is also increasing. Among them, similarity retrieval for pictures is widely used because it can realize automatic ranking of network pictures, recommendation of related pictures and other effects. [0003] Currently, image similarity retrieval methods include tree similarity algorithms, such as R-tree, Kd-tree, and SR-tree, which are based on space division; [0004] The linear retrieval method scans the pictures in the entire database sequentially, calculates the Euclidean distance to the pictures to be retrieved, and then outputs them sequentially; [0005] LSH (Location Sensitive Hash, Location Sensitive Hash Function) algo...

AI Technical Summary

Problems solved by technology

[0007] At present, the time efficiency of the retrieval results of the tree-like similarity algorithm on high-dimensional data sets is not high; the linear retrieval method is accurate in data but the time complexity is too high, especially when the performance of massive retrieval is poor; although the LSH algorithm guarantees time and space complexity It can well support the retrieval of high-dimensional data while having a low degree of accuracy, but there are often many retrieved results, which prevents users from retrieving the most similar images.

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