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. 26, 2022

Filed:

Mar. 26, 2018
Applicant:

Huawei Technologies Co., Ltd., Guangdong, CN;

Inventors:

Qing Zhang, Beijing, CN;

Miao Xie, Shenzhen, CN;

Shangling Jui, Shanghai, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 7/0002 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01);
Abstract

This application provides an image aesthetic processing method and an electronic device. A method for generating an image aesthetic scoring model includes: constructing a first neural network based on a preset convolutional structure set; obtaining an image classification neural network, where the image classification neural network is used to classify image scenarios; obtaining a second neural network based on the first neural network and the image classification neural network, where the second neural network is a neural network containing scenario information; and determining an image aesthetic scoring model based on the second neural network, where output information of the image aesthetic scoring model includes image scenario classification information. In this method, scenario information is integrated into a backbone neural network, so that a resulting image aesthetic scoring model is interpretable. In addition, scoring accuracy of the image aesthetic scoring model can be improved by using the preset convolutional structure set.


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