High-dimension vector rapid searching algorithm based on block distance
A block distance, vector technology, applied in the field of data processing
Inactive Publication Date: 2013-09-04
COMMUNICATION UNIVERSITY OF CHINA
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[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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[0031] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings:
[0032] The technical solution of this embodiment is as follows figure 1 (a) shows:
[0033] First, select a reference point from the high-dimensional vector set; then calculate the block distance between each high-dimensional vector and the reference point in the high-dimensional vector set one by one, and obtain the key value corresponding to each high-dimensional vector; Insert the corresponding key value to get the BlockB-tree (such as figure 1 (b), the upper layer is B + -tree, each key value of the leaf node layer is bound to a pointer to the corresponding high-dimensional vector). When searching, calculate the block distance between the query vector and the reference point, obtain the query key value, and locate the position where the query key value should be inserted at the leaf node layer of the BlockB-tree, and obtain the query ...
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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 structure provided 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 multimedia information retrieval, intelligent information processing, data mining and other data processing fields, 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, massive multimedia data has been generated. How to quickly find the required information in the massive multimedia database is a key issue in the current research in the field of multimedia databases. The traditional method is to manually mark multimedia data, and then realize multimedia information retrieval through text retrieval. However, manual annotation has the defects of heavy workload and strong subjectivity. For the explosive growth of multimedia data, complete manual annotation is unrealistic. Therefore, content-based multimedia information retrieval technology needs to be studied. [0003] The tec...
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IPC IPC(8): G06F17/30
Inventor 黄祥林杨丽芳吕锐吕慧
Owner COMMUNICATION UNIVERSITY OF CHINA