Data search method and system

Abstract

The invention provides a data search method. The data search method comprises the steps of extracting the feature vector of a training dataset; projecting the feature vector into a preset feature space approximate to a Hamming space; obtaining a quantized threshold value according to the projection algorithm and determining the optimal buffer area according to the optimization performance and the optimization range; carrying out double-bit quantification on the feature vector projection result according to the quantized threshold value and the optimal buffer area to obtain a Hash code; obtaining a Hash code of a search case, and according to the Hash code of the search case and the Hash code of the feature vector projection result, extracting approximate training data from the training data set as the research result of the search case. The data search method has the advantages of being high in search speed and high in search precision. The invention further provides a data search system.

Description

technical field [0001] The invention relates to the technical field of electronic information, and in particular relates to a data search method and system. Background technique [0002] In recent years, Internet data has shown an explosive growth trend. Users continue to upload information, images, videos, music and other data to the Internet through various channels, such as Facebook and Twitter abroad, Renren and Weibo in China. etc., in order to realize the storage and sharing of information. Such large-scale data growth has brought many challenges to the Internet field, including how to store these data and how to perform fast and efficient queries on these data. Especially for unstructured data such as images, its content information is often expressed as a high-dimensional feature vector, and in such a massive and rapidly growing data, using the Euclidean distance equidistance measurement method and the traditional traversal nearest neighbor search will It consumes ...

AI Technical Summary

Problems solved by technology

In actual operation, the threshold value is usually taken as the mean or median of the projection values ​​of each data point in the data set on this dimension, and the distribution of data points near the threshold value is often dense, which is easy to cause adjacent data near the threshold value Points are given different codewords, which destroys the neighborhood distribution structure of the original feature space

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