Construction method of dynamic k-nearest neighbor graph and rapid image retrieval method based on dynamic k-nearest neighbor graph

Abstract

The invention relates to a construction method of a dynamic k-nearest neighbor graph and a rapid image retrieval method based on the dynamic k-nearest neighbor graph, which realize online updating ofan approximate k-nearest neighbor graph and realize rapid image retrieval based on the dynamic k-nearest neighbor graph, and because distance measurement between vectors is not assumed, the method hasgood generalization, and moreover, the mapping efficiency exceeding that of a nearest neighbor descent method is displayed on most of data sets while the quality of the k-nearest neighbor graph is ensured, and the retrieval efficiency on a plurality of image feature data sets is better than that of widely accepted methods such as HNSW and the like.

Description

technical field [0001] The invention relates to the technical field of information retrieval, in particular to a method for constructing a dynamic k-nearest neighbor graph and a fast image retrieval method based on the dynamic k-nearest neighbor graph, which can be applied to scenarios such as e-commerce, search engines, and installation monitoring. Background technique [0002] The k-nearest neighbor graph is a directed graph. For a given vector set S={x|x∈R d}, each vertex of the directed graph represents a vector in the vector set, and each vertex has k edges, pointing to the vertices (k nearest neighbors) to which the k closest vectors belong to, and the similarity is determined by the distance between the vectors Decision, commonly used are Euclidean distance, Hamming distance, cosine distance and so on. The k-nearest neighbor graph is an important data structure in the fields of manifold learning, computer vision, machine learning and multimedia information retrieval....

AI Technical Summary

Problems solved by technology

However, in actual scenarios, the data set sometimes changes. In this case, it is impossible to construct a high-quality k-nearest neighbor graph by using the nearest neighbor descent method.

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