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Picture classification model cold start method, system and device and medium

A technology of image classification and cold start, applied in biological neural network models, character and pattern recognition, instruments, etc., can solve problems such as difficulty in obtaining a large number of image classification models, achieve classification accuracy, realize data accumulation, and overcome training data hard-to-get effects

Active Publication Date: 2021-08-24
SHANGHAI QIYUE INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above-mentioned defects in the prior art, the present invention provides a technical solution for a method, system, device and medium for cold-starting an image classification model, aiming to solve the technical problem of how to realize the cold-start of an image classification model to avoid difficulty in obtaining a large number of The technical problem of the data of the image classification model; further, to solve the technical problem of how to realize the cold start accumulation data more efficiently and conveniently, so as to reduce various costs and resource consumption when obtaining data

Method used

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  • Picture classification model cold start method, system and device and medium
  • Picture classification model cold start method, system and device and medium
  • Picture classification model cold start method, system and device and medium

Examples

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Embodiment 1

[0043] Combine below figure 1 The main flow chart of an embodiment of the method for cold-starting the image classification model according to the technical solution of the present invention is shown to illustrate the implementation process of the cold-start of the image classification model in the present invention. In this embodiment, the cold start of the CNN model is mainly helped by pre-SIFT feature matching.

[0044] Step S110, classify the pictures in the picture set based on SIFT feature matching.

[0045] For example, for a set of scene pictures in a target video, picture classification is performed based on SIFT feature matching.

[0046] Among them, several pictures can be selected from the target video in a predetermined time interval to form a scene picture set, and then the pictures in the scene picture set are classified through the SIFT feature matching algorithm to obtain the classified pictures. Wherein, the predetermined time interval is every second, and ...

example 1

[0066] Example 1: Taking a single classification as an example, for the first classification, the image is classified through SIFT feature matching, that is, the SIFT algorithm is used 100%; the classified image is used as training data to train the initial image classification model such as CNN0, and CNN1 is obtained. New model; for the second time, the SIFT algorithm and CNN0 can be used to continue to classify other unclassified pictures. The proportion of using the SIFT algorithm is 67%, while the proportion of using CNN0 is 33%; the classified pictures are used as training data for CNN1 That is, the new model (the model whose parameters have been updated relative to the last previous model, or the updated model) is trained to obtain CNN2, which is the new model; the third time still uses the SIFT algorithm and CNN1 to continue unclassified other pictures Classification, the proportion of using SIFT algorithm is 33%, while the proportion of using CNN1 is 33%; the classified...

example 2

[0067] Example 2: Taking a single classification as an example, it is also possible to classify pictures through SIFT feature matching during the first classification, that is, 100% use the SIFT algorithm; use the classified pictures as training data to train the initial picture classification model such as CNN0 , get CNN1 which is the new model; the second time you can use SIFT algorithm and CNN1 to continue to classify other unclassified pictures, the proportion of using SIFT algorithm is 67%, while the proportion of using CNN0 is 33%; the classified pictures are used as The training data trains CNN1, which is a new model relative to the initial model CNN0 (the model whose parameters have been updated relative to the last previous model, or the updated model), to obtain CNN2, which is the new model; the third time is still used at the same time The SIFT algorithm and CNN2 continue to classify other unclassified pictures. The proportion of using SIFT algorithm is 33%, while th...

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Abstract

The invention relates to the field of picture classification, and aims to overcome the defects that when images / pictures such as videos are used for classification to extract information in an existing risk control scene, a mode for obtaining specific pictures and classification needs a large amount of data and causes large consumption of various resources and costs. The invention provides a picture classification model cold start method, system and device and a medium, and aims to solve the technical problem of how to realize the picture classification model cold start so as to avoid the defect that it is difficult to obtain a large amount of training data of a picture classification model. Therefore, picture classification of cold start accumulated data can be realized more efficiently and conveniently, and various costs and resource consumption during data acquisition can be effectively reduced. According to the method, pictures of a risk control scene are matched and classified through pre-SIFT features to serve as training data to train the picture classification model, and the pictures are classified in two modes according to the changing use proportion so as to complete cold start of the picture classification model.

Description

technical field [0001] The present invention relates to the field of image classification, in particular, to a method, system, device and medium for cold-starting an image classification model. Background technique [0002] In risk control or risk control scenarios, it is often necessary to extract specific text information (such as name, age, etc.) from images such as videos for use in risk control. Generally, this information is in different application scenarios, such as different pictures in a video, so it is necessary to extract this information from a specific picture. In this regard, in the prior art, most of the methods of obtaining these specific pictures from images such as videos can be classified by using a CNN model, for example, to classify the pictures obtained for each frame in the video. Although the accuracy of this method is high, it requires a large amount of data when training models such as CNN, and these data often need to be obtained manually. It is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62G06N3/04
CPCG06V10/462G06N3/045G06F18/22G06F18/214
Inventor 张彤彤王守一
Owner SHANGHAI QIYUE INFORMATION TECH CO LTD