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Feature retrieval method and device, storage medium and computer device

A feature collection and feature matching technology, applied in the field of information services, can solve the problems of reduced retrieval accuracy, large amount of information, and slow processing speed

Active Publication Date: 2019-01-25
SHENZHEN SENSETIME TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, when performing feature retrieval, the input features to be retrieved are searched among 1.4 billion known features, and the feature itself contains a relatively large amount of information, resulting in a very slow processing speed
[0003] In related technologies, by matching the known compressed features after the known features are compressed with the compressed features corresponding to the features to be retrieved, the known features corresponding to the matched known compressed features are used as the final retrieval result. In this way, by compressing Feature retrieval improves retrieval efficiency, but greatly reduces retrieval accuracy

Method used

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  • Feature retrieval method and device, storage medium and computer device
  • Feature retrieval method and device, storage medium and computer device
  • Feature retrieval method and device, storage medium and computer device

Examples

Experimental program
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Embodiment 1

[0116] This embodiment provides a feature retrieval method, such as figure 2 As shown, the method includes the following steps:

[0117] S201. Perform feature extraction on features to be retrieved to obtain compressed features to be retrieved;

[0118] When the retrieval device receives the retrieval operation, it responds to the retrieval operation and generates the features to be retrieved according to the retrieval image corresponding to the retrieval operation, wherein the retrieval feature is the feature information of the retrieval object included in the retrieval image, and the retrieval object can be a human face, a vehicle, etc. and other objects. The retrieval device generates a retrieval request according to the retrieval feature, and sends the generated retrieval request to the service node.

[0119] After receiving the retrieval request, the service node analyzes the retrieval request to obtain the features to be retrieved carried by the retrieval request, and...

Embodiment 2

[0173] In the embodiment of the present invention, by image 3 The network structure shown further illustrates the feature retrieval method provided by the embodiment of the present invention. image 3 The network structure shown comprises: interface proxy service (shard-proxy) 301, service node 302, database 303 and object store 304; There are compression features used by workers for retrieval. Each replica set (ReplicaSet) includes two service nodes: the master service node and the slave service node, where the worker of the master service node is the master process (master), the worker of the slave service node is the slave process (slave), and the master service node The first subset is stored in the video memory of the server node, and the second subset is stored in the video memory of the slave service node. The first subset of master service nodes and the second subset of slave service nodes form a replica set.

[0174] next to image 3 Each component shown is descr...

Embodiment 3

[0256] In the embodiment of the present invention, four retrieval methods are used to compare the feature retrieval method in the related art with the feature retrieval method provided in the embodiment of the present invention, wherein, method 1 and method 2 are the feature retrieval methods in the related art, method 3 and method 4 are feature retrieval methods provided by the embodiments of the present invention.

[0257] Method 1. Original Feature Retrieval

[0258] Figure 7A It is a schematic diagram of feature retrieval method 1 in the related art, such as Figure 7A As shown, the original feature array in the database includes multiple original features. When searching for the features to be retrieved, the features to be retrieved are matched with each original feature in the original feature array to find the target candidate that matches the features to be retrieved. feature.

[0259] Method 2, compressed feature retrieval

[0260] Figure 7B It is a schematic d...

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Abstract

The embodiment of the invention provides a feature retrieval method and a device, a storage medium and a computer device, wherein, the method comprises the following steps: extracting features to be retrieved to obtain compressed features to be retrieved; finding a target compression feature set matching the compression feature to be retrieved from a replica set, the target compression feature setcomprising at least one target compression feature, the replica set comprising different compression features; determining candidate features corresponding to each target compression feature from theoriginal feature set to form a candidate feature set; the original feature set including at least one original feature; comparing the candidate features in the candidate feature set with the features to be retrieved to obtain the target candidate features corresponding to the features to be retrieved.

Description

technical field [0001] The invention relates to the field of information services, in particular to a feature retrieval method and device, a storage medium and computer equipment. Background technique [0002] The feature retrieval service is to find the features that match the input features to be retrieved in a series of known features. A series of existing known features are stored in the database, but feature-based retrieval services are usually used in intelligent video analysis, security monitoring and other fields. The known features stored in the database are massive, such as: National Citizen Face Information Database The facial features stored in the database are the facial features of 1.4 billion citizens across the country, including as many as 1.4 billion known features. Therefore, when performing feature retrieval, the input features to be retrieved are searched among 1.4 billion known features, and the feature itself contains a relatively large amount of info...

Claims

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Application Information

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IPC IPC(8): G06F16/58G06F16/78
Inventor 陈宇恒樊俊良
Owner SHENZHEN SENSETIME TECH CO LTD
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