Locality sensitive hash image retrieval parameter optimization method based on empirical fitting

A local sensitive hash and sensitive hash function technology, which is applied in the field of image processing, can solve problems such as errors, and achieve the effects of improving operating efficiency, reducing calculation steps, and reducing complexity

CN108959427AActive Publication Date: 2018-12-07NANJING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2018-12-07

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Abstract

The invention relates to a locality sensitive hash image retrieval parameter optimization method based on empirical fitting, which comprises the following steps of: S1, defining a locality sensitive hash function family H; S2, assuming that k is the number of locality sensitive hash functions and L is the number of hash index tables, when values of L, r and w are determined, calculating a value ofk; S3, taking k functions from the H, and defining a k-dimensional locality sensitive ash function family G; and S4, taking L hash functions from the G, and establishing L hash index tables. According to the invention, a locality sensitive hash image retrieval parameter optimization empirical formula is obtained by a regression analysis method, and by using the empirical formula, calculation steps can be effectively reduced, complexity of parameter optimization of an algorithm can be reduced, and operation efficiency of the algorithm can be improved. Meanwhile, the locality sensitive hash image retrieval parameter optimization method disclosed by the invention is approximate to the theoretical optimum and can enable the algorithm to obtain a high F1 so as to obtain excellent algorithm performance.
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Description

technical field

[0001] The invention relates to a parameter optimization method, in particular to a local sensitive hash image retrieval parameter optimization method based on experience fitting, which belongs to the field of image processing. Background technique

[0002] With the advent of the data age, the processing volume of multimedia data such as images, videos, and audios on the Internet has increased dramatically. The feature dimensions to be extracted from image, video and other data reach hundreds of dimensions or even thousands of dimensions, and these high-dimensional data often show unstructured characteristics. When dealing with high-dimensional data, traditional data processing methods cannot meet the requirements. Algorithms such as data retrieval and semantic analysis pose enormous difficulties. The content-based image retrieval method does not rely on keywords to search, but performs image matching by extracting content features of images. Among them, th...

Examples

Embodiment Construction

[0033] Such as figure 1 As shown, the present invention discloses a locality-sensitive hash image retrieval parameter optimization method based on empirical fitting.

[0034] Specifically, a locality-sensitive hash image retrieval parameter optimization method based on empirical fitting includes the following steps:

[0035] S1. Define a locality-sensitive hash function family H.

[0036] S2. Let k be the number of local sensitive hash functions, and L be the number of hash index tables. When the values ​​of L, r, and w are determined, the value of k is calculated. In this embodiment, the value range of L is [1, 1000], and the larger the value of L, the better the effect of the present invention. The w>r.

[0037] S3. Take k functions from H, and define a family G of k-dimensional locality-sensitive hash functions.

[0038] S4. Take L hash functions from G, and create L hash index tables.

[0039] The definition of locality-sensitive hash function family H described in S1...