Image processing method and device

Abstract

The invention discloses an image processing method and device. The method comprises the following steps: determining a first clustering center corresponding to a first feature vector; wherein the first feature vector is a feature vector of a query sample, the first clustering center is obtained according to a product quantization distance between the first feature vector and the second clusteringcenter, the second clustering center is a clustering center of each feature sub-vector in N feature sub-vectors included in the second feature vector, and the second feature vector is a feature vectorof a sample library; calculating a Hamming distance of a second clustering center of the first clustering center; determining a sample set with high similarity with the query sample from a sample library according to the Hamming distance; determining a target sample from the sample set according to the product quantization distance of the first feature vector and the third feature vector; whereinthe target sample is the sample with the highest similarity with the query sample, and the third feature vector is the feature vector of each sample in the sample set. By adopting the method, the query efficiency of the query sample can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an image processing method and device. Background technique [0002] With the development of big data, how to filter images similar to query samples from a large number of library samples needs to be solved urgently. [0003] Generally, a product quantization (PQ) method can be used. Specifically, the feature space of the sample image can be decomposed into a Cartesian product of multiple low-dimensional subspaces, and then each subspace can be quantized separately. More specifically, in the training phase, each subspace can be clustered to obtain k cluster centers, and the Cartesian product of the cluster centers of all subspaces constitutes a dense division of the whole space. After quantitative learning, for a given query sample, the asymmetric distance between the query sample and the sample in the library can be calculated by looking up the table, so that an ima...

AI Technical Summary

Problems solved by technology

[0004] However, the method shown above is very computationally intensive and the query efficiency is low

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