Similarity network fast fusion method used for data clustering

Abstract

The invention provides a similarity network fast fusion method used for data clustering. The method includes the steps of ahead-of-time training and storage of similarity networks and fast fusion of the similarity networks. According to the ahead-of-time training and storage of the similarity networks, distances between samples are utilized to calculate similarity networks of different characteristics of the samples, and the networks which are obtained in the calculation process are saved so as to be subjected to similarity network fast fusion which is performed on new samples. According to the similarity network fast fusion method used for data clustering of the invention, the similarity networks are constructed as different types of data similarity networks, so that the networks can be effectively fused; the categories of new targets can be effectively and fast predicted according to the fused networks and a part of calibrated samples; time complexity in online prediction can be effectively reduced through matrix partition and pre-processing; and only a small amount of precision ratio is subjected to loss. The similarity network fast fusion method used for data clustering can be applied to videos, and online target classification can be realized with high accuracy and high speed.

Description

technical field [0001] The invention relates to a data fusion method, in particular to a fast method for similarity network fusion for data clustering. Background technique [0002] At present, intelligent surveillance video has attracted a lot of attention due to its wide range of applications. In intelligent video technology, automatic object detection and classification has become a main task of intelligent surveillance system. Surveillance video object classification has become a challenging task due to the complexity of motion, interlaced backgrounds, changes in object positions and viewing angles, etc. At the same time, real-time online target classification is often required in actual monitoring systems. [0003] Among the existing object classification methods, some methods use shape and motion information for classification. This type of method is relatively fast, but its robustness is also limited because the shape of the object in the video will vary with the vi...

AI Technical Summary

Problems solved by technology

[0005] Although the similarity network fusion method has many advantages, it is not suitable for the target online classification due to its slow classification speed for a single new sample.

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