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....

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