The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 05, 2022

Filed:

Apr. 20, 2020
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Pai Peng, Shenzhen, CN;

Kailin Wu, Shenzhen, CN;

Xiaowei Guo, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2022.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06N 20/10 (2019.01); G06N 3/08 (2006.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06K 9/6277 (2013.01); G06K 9/6215 (2013.01); G06K 9/6232 (2013.01); G06K 9/6289 (2013.01); G06N 3/084 (2013.01); G06N 20/10 (2019.01); G06T 5/50 (2013.01); G06T 7/0002 (2013.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01);
Abstract

An image classification method includes: obtaining an original image and a category of an object included in the original image; adjusting a first display parameter of the original image to obtain an adjusted original image; and transforming a second display parameter of the original image to obtain a new image. The adjusted first display parameter satisfies a value condition; and the transformed second parameter of the new image satisfies a distribution condition. The method also includes training a neural network model based on the category of the included object and a training set constructed by combining the adjusted original image and the new image; and inputting a to-be-predicted image into the trained neural network model, and determining the category of the object included in the to-be-predicted.


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