Hash model training method, similar object retrieval method and device

Abstract

The invention provides a hash model training method, a similar object retrieval method and device. A multilayer neighborhood pyramid is constructed by gradually increasing the number of neighbor points. And in each layer of neighborhood of the neighborhood pyramid, the average distance from the neighbor point in the layer to the reference point is taken as the Euclidean neighborhood measure of thelayer of neighborhood. And data points in the original space are mapped to a Hamming space by utilizing a hash model, and Hamming neighborhood measure of each layer of the pyramid is calculated. Theoptimization target of the hash model is to keep neighborhood measurement in an original space in a Hamming space, the optimization target can keep distance distribution of real neighbor points and also can keep neighbor sorting, finally better distance keeping is obtained, and then the accuracy of approximate nearest neighbor retrieval is improved. The feature vector of the Hamming space of the object obtained by using the hash model can better maintain the features of the object in the Euclidean space, so that the similarity retrieval accuracy can be improved by using the method.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a hash model training method, an image retrieval method and a device. Background technique [0002] With the rapid growth of multimedia data scale, approximate nearest neighbor search technology is widely used in computer vision and image processing. Given a query sample, the approximate nearest neighbor search technique can find the real nearest neighbor of the query sample from a large-scale data set with a high probability, and the retrieval time complexity is linear or even constant time complexity. [0003] The binary hash method in the approximate nearest neighbor search technology maps high-dimensional data points in the original space to low-dimensional binary codes in the Hamming space through distance-preserving constraints or semantic similarity constraints. Binary codes can greatly reduce storage overhead, and the Hamming distance between binary c...

AI Technical Summary

Problems solved by technology

Although existing unsupervised hashing methods have shown promising retrieval performance on public datasets, ...

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