Hash learning method based on an evolutionary tree and an unsupervised online Hash learning method thereof

Abstract

The invention relates to a Hash learning method based on an evolutionary tree. the evolution tree is trained through data points in the data set; obtaining a trained evolution tree; carrying out initialization Hamming code coding on all nodes except a root node in the trained evolution tree, optimizing a similarity protection loss function of the whole evolution tree by using a greedy path codingstrategy, and taking Hamming code coding corresponding to a minimum value of the similarity protection loss function as Hash coding of each leaf node of the evolution tree; and calculating an optimalmatching point of a certain data point in the evolution tree, finding a splitting path of a leaf node corresponding to the optimal matching point of the data point split from the root node, orderly combining Hash codes of the corresponding leaf node in the splitting path of the optimal matching point of the data point, and taking the Hash codes as Hash codes of the data point. The invention also discloses an unsupervised online Hash learning method. According to the Hash method, the coding complexity can be reduced, and the good query performance is achieved.

Description

technical field [0001] The invention relates to the field of data processing, in particular to an evolution tree-based hash learning method and an unsupervised online hash learning method thereof. Background technique [0002] With the rapid development of the Internet and various electronic devices, various types of data, such as text, images and videos, are increasing rapidly. In many application scenarios, people need to retrieve relevant content from such large-scale data. However, in large-scale data, the computation time spent to find the exact nearest neighbor for a given query point is unacceptable. In order to solve this problem, a large number of recent studies have been devoted to similar nearest neighbor (Approximate Nearest Neighbor, ANN) search. In large-scale data, the effect of ANN search can replace the exact nearest neighbor search, and the speed is very fast. ANN retrieval based on hash learning is one of the more well-known ANN retrieval technologies. I...

AI Technical Summary

Problems solved by technology

Supervised hashing algorithms mainly include RBM, BRE, MFH, IMH, and MLH. Although supervised hashing shows higher search accuracy than unsupervised hashing, their training requires label information. In the era of massive data , the data scale is large, the update speed is fast, and the acquisition of data labels often requires huge labor costs, so unsupervised hashing is more meaningful in practical applications

However, most unsupervised hashing algorithms need to load all the data at one time, which will take up a lot of memory, cannot be applied to streaming data, and there are few related studies

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