A Data Retrieval Method Based on Adaptive Binary Quantized Hash Coding

A binary coding, data retrieval technology, applied in other database retrieval, other database indexing, special data processing applications, etc., can solve the problem of poor retrieval effect, difficult to maintain spatial relationship, etc., to maintain the nearest neighbor structure, high search freedom degree, the effect of reducing quantization loss

Active Publication Date: 2020-05-29
BEIHANG UNIV
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Problems solved by technology

[0004] However, most of the current methods try to establish a complete match between the data space and the coding space, and the data distribution usually does not satisfy the regular hypercube structure in the coding space, so these methods are difficult to maintain the spatial relationship, so the retrieval effect is not good

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  • A Data Retrieval Method Based on Adaptive Binary Quantized Hash Coding
  • A Data Retrieval Method Based on Adaptive Binary Quantized Hash Coding
  • A Data Retrieval Method Based on Adaptive Binary Quantized Hash Coding

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[0023] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0024] At present, in order to better classify data, the concept of clustering is introduced, that is, the data in the entire data space is identified according to the attributes of the data, and the entire data space is classified into several categories according to the similarity of the data to form several clusters. Class center. Each cluster center represents the data belonging to the cluster center, and each cluster center has a corresponding binary code in the corresponding Hamming space. Therefore, for each data in the data space, it corresponds to the binary code corresponding to the cluster center in the Hamming space.

[0025] Currently, given a binary coded bit number b, up to 2 can be generated b A ...

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Abstract

The present invention provides a data retrieval method based on adaptive binary quantized hash coding, comprising: S1, selecting cluster centers in the data space to form a cluster center set, and assigning a Hamming space to each cluster center The binary code in the binary code is obtained to obtain the corresponding binary code set; S2, according to the obtained binary code set, update the cluster center set and the cluster center to which the data in the data space belongs, until the data space is consistent with the sea Align the bright space, and map all the data in the data space to the binary code corresponding to the cluster center to which it belongs, so as to complete the hash coding. A data retrieval method based on adaptive binary quantization hash coding provided by the present invention adopts an incomplete coding scheme to adaptively determine the number of cluster centers and the corresponding binary coding, thereby reducing quantization loss and improving hash coding. retrieval performance.

Description

Technical field [0001] The present invention relates to the field of computer data retrieval, and more specifically, to a data retrieval method based on adaptive binary quantization hash coding. Background technique [0002] At present, with the advent of the big data era, massive amounts of unstructured data such as images, videos, etc. are being generated on the Internet at all times. How to establish efficient retrieval algorithms for these data has become an urgent problem to be solved. In practical applications, people often need to query the most similar objects of a given sample, and the data is usually expressed as a feature vector obtained by feature extraction. For example, the local features of an image use a scale-invariant feature transform (SIFT) algorithm. The feature vector is obtained, and the global feature uses the generalized search tree algorithm (Generalized SearchTrees, GIST) to obtain the feature vector, so the problem is transformed into the nearest neigh...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/901G06K9/62
CPCG06F16/9014G06F18/2321
Inventor 刘祥龙夏柯
Owner BEIHANG UNIV
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