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Product quantification method based on fuzzy clustering and asymmetric distance calculation

A technology of fuzzy clustering and quantitative methods, applied in computing, computer components, still image data clustering/classification, etc., can solve the problems of low image search speed and accuracy, and achieve objective similarity measurement results and correlation High, improve the effect of retrieval accuracy

Active Publication Date: 2022-07-22
中船凌久高科(武汉)有限公司
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Problems solved by technology

[0005] In view of the above problems, the purpose of the present invention is to provide a product quantification method based on fuzzy clustering and asymmetric distance calculation, aiming to solve the technical problem that the image search speed and precision of the existing method are not high

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  • Product quantification method based on fuzzy clustering and asymmetric distance calculation
  • Product quantification method based on fuzzy clustering and asymmetric distance calculation
  • Product quantification method based on fuzzy clustering and asymmetric distance calculation

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Embodiment Construction

[0037] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0038] In order to illustrate the technical solutions of the present invention, the following specific embodiments are used for description.

[0039] figure 1 The flow of the product quantization method based on fuzzy clustering and asymmetric distance calculation provided by the embodiment of the present invention is shown, and only the part related to the embodiment of the present invention is shown for the convenience of description.

[0040] Step S1, perform initial clustering on the original vector set, calculate its residual vector set for each cluster, divide the residual vector in...

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Abstract

The invention is suitable for the field of computer vision and big data, and provides a fuzzy clustering and asymmetric distance calculation-based product quantization method, which comprises the following steps of: firstly, carrying out initial clustering on an original vector set, calculating a residual vector set, and carrying out fuzzy clustering on residual sub-vectors to obtain a membership matrix; carrying out sub-vector classification according to the membership degree; carrying out weighted distance calculation on the query vector according to a corresponding mode, and counting the sum of the distance ranking serial numbers of each sub-segment; according to the method, a fuzzy clustering method is applied to product quantization index subspace vector clustering, so that errors caused by non-uniform sample category distribution in hard division are avoided, and vector clustering is more objective; weighted asymmetric distance calculation and distance ranking statistical methods are adopted in similarity measurement, and retrieval errors caused by overlarge calculation of a certain distance of a vector are avoided. Compared with an original product vector method, the method provided by the invention has higher precision in retrieval of complex background pictures.

Description

technical field [0001] The invention belongs to the technical field of computer vision and big data, and in particular relates to a product quantification method based on fuzzy clustering and asymmetric distance calculation. Background technique [0002] With the rapid development of computers and the Internet, the information on the Internet has also exploded. In specific practical fields such as smart transportation and smart security, hundreds or thousands of cameras and optoelectronic devices are frequently connected, and these machines converge into a large-scale monitoring network. In large-scale scenic spots, shopping malls and other scenarios, hundreds of terabytes of video data are often generated in one day, so solving the problem of quickly and accurately searching for target pictures in such a large amount of video data will bring great practical application value. [0003] Content-based image retrieval technology is mainly divided into two types: one is the pr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/583G06F16/55G06V10/74G06V10/762G06K9/62
CPCG06F16/583G06F16/55G06F18/22G06F18/23213
Inventor 刘鑫杨志祥丁雪峰程佳斌余将其皮辉杨小涛熊筠轲谈俊郭朝霞许雷范俊甫蔡烨彬谢倩
Owner 中船凌久高科(武汉)有限公司
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