High-dimension vector rapid searching algorithm based on block distance

Abstract

The invention provides a high-dimension vector rapid searching algorithm based on a block distance and belongs to the field of data processing such as multimedia information searching, intelligent information processing, data mining, and the like. In the invention, an index structure Block B-tree which is converted from high dimension to one dimension and is based on the block distance is provided; a high-dimension vector is mapped into one-dimensional key values by adopting the block distance of the high-dimension vector to a reference point; and the index structure B+-tree is used for managing the key values, and each key value of a leaf node layer is bound with a pointer pointing to a corresponding high-dimension vector. During searching, the same mapping method is used for mapping a query vector into one-dimension query key values, and then similarity calculation is only performed on the high-dimension characteristics of the key values close to the query key values, thereby reducing the calculated quantity and greatly increasing the searching speed. In a similarity matching algorithm of the high-dimension vector, the block distance is a frequently-used measurement way, the operation of the algorithm is simple, and the searching efficiency is higher, while most of the current index structures are provided based on Euclidean distance matching measurement. The index structureprovided by the invention not only supports searching based on the Euclidean distance matching way but also directly supports searching based on the block distance measurement way.

Description

technical field [0001] The invention belongs to the fields of data processing such as multimedia information retrieval, intelligent information processing, and data mining, and specifically relates to a high-dimensional vector fast retrieval algorithm based on block distance. Background technique [0002] With the development of computer and information technology, a large amount of multimedia data has been produced. How to quickly find the required information in the massive multimedia database is a key issue in the field of multimedia database research. The traditional method is to manually mark the multimedia data, and then realize the multimedia information retrieval through text retrieval. However, manual labeling has the disadvantages of heavy workload and strong subjectivity. For the explosive growth of multimedia data, manual labeling is impossible. Therefore, it is necessary to study content-based multimedia information retrieval technology. [0003] The technical ...

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Problems solved by technology

[0007] Block distance is one of the most commonly used measurement methods in high-dimensional vector similarity matching algorithms. Its operation is simple and has high retrieval efficiency. However, most of the previously proposed high-dimensional to one-dimensional index structures are based on Euclidean distance matching. proposed by the measure, there is no one that can directly support the measure of block distance

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