Query method based on multi-feature fusion type multiple Hashtables

Abstract

The invention provides a query method based on a multi-feature fusion type multiple Hashtables. The query method comprises the steps that various features are extracted through training data, and at least one anchor point of each feature is generated; an anchor point diagram corresponding to each of the various extracted features is generated according to the at least one anchor point of the feature; the similarity of each feature is calculated according to the corresponding anchor point diagram of the feature; the Hash function of each feature and the combination coefficient of each anchor point diagram are learnt in sequence according to the similarities of the features, and a similarity matrix of each feature is updated; Hash coding is conducted on any data according to the learnt Hash functions of the features and the learnt combination coefficients, the multiple Hashtables are established, and data query is conducted according to the multiple Hashtables. According to the query method based on the multi-feature fusion type multiple Hashtables, rapid nearest neighbor searching is achieved, memory consumption is low, query efficiency is effectively improved, and the accuracy rate is effectively increased.

Description

technical field [0001] The invention relates to the technical field of computer search, in particular to a multi-hash table query method based on multi-feature fusion. Background technique [0002] The rapid growth of data such as pictures and videos has brought great challenges to data indexing and searching. Approximate nearest neighbor search on big data has attracted more and more attention in the past decades due to its excellent performance in retrieval performance and efficiency. Hash-based nearest neighbor search has been successful in practice, and at the same time there are solid performance guarantees in theory due to its locality-sensitive features. [0003] The hash-based nearest neighbor search technology has a wide range of application prospects, especially in practical applications, in order to improve the overall performance of the search, it is often necessary to establish multiple hash tables, and use the detected data in multiple hash buckets as Returns...

AI Technical Summary

Problems solved by technology

[0005] The existing hash-based nearest neighbor search technology often needs to establish multiple hash tables in order to improve the overall performance of the search, and returns the data in the detected multiple hash buckets as the result, resulting in large memory consumption. Long response time and other issues, which in turn affect query efficiency

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