Neighbor searching method and neighbor searching system

Abstract

The invention discloses a neighbor searching method and a neighbor searching system. The neighbor searching method includes offline learning and online searching. A learning function of data point number in small regions on two sides of a Hash hyperplane is minimized to enable the Hash hyperplane to penetrate through a data sparse region, and accordingly high accuracy in neighbor searching can be guaranteed. An approximate balance barrel regular term is added to a learning function through approximate balance barrel conditions to enable more balanced data point partition by the Hash hyperplane, and accordingly high searching speed in neighbor searching is guaranteed. Regardless of a small quantity of data or massive data, high-accuracy and high-speed neighbor searching can be realized by the neighbor searching method and the neighbor searching system.

Description

technical field [0001] The invention relates to the technical field of information retrieval, in particular to a neighbor search method and system. Background technique [0002] With the rapid development of information technology and the improvement of data collection capabilities, the volume and dimension of data in various fields have increased exponentially. However, the rapid growth of data volume and data dimensions makes searching extremely difficult. For example, for an input picture, when we need to find pictures that are the same or similar to this picture in a massive picture library, we must not only find it accurately, but also find it quickly. In this example, we can abstract the image into data points. The similarity between data points can usually be measured by Euclidean distance. Nearest neighbor search refers to finding similar data points with the query data points under this similarity measure. [0003] If the query data points are compared with all po...

AI Technical Summary

Problems solved by technology

[0003] If the query data points are compared with all points in the database one by one, although the retrieval accuracy can be guaranteed, it will become very slow for massive high-dimensional data

If the traditional tree-based neighbor search technology wants to get a relatively high accuracy rate, the retrieval speed will drop rapidly as the data dimension increases.

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