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:
Jun. 16, 2020

Filed:

Mar. 30, 2016
Applicant:

Institute of Automation, Chinese Academy of Sciences, Beijing, CN;

Inventors:

Kaiqi Huang, Beijing, CN;

Tieniu Tan, Beijing, CN;

Ran He, Beijing, CN;

Yueying Kao, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 7/00 (2006.01); G06K 9/00 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06K 9/62 (2006.01); H04N 17/00 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); G06K 9/00664 (2013.01); G06K 9/6262 (2013.01); G06K 9/6278 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 3/084 (2013.01); G06N 7/005 (2013.01); G06K 9/6267 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30168 (2013.01); H04N 17/00 (2013.01);
Abstract

The present application discloses a method for assessing aesthetic quality of a natural image based on multi-task deep learning. Said method includes: step 1: automatically learning aesthetic and semantic characteristics of the natural image based on multi-task deep learning; step 2: performing aesthetic categorization and semantic recognition to the results of automatic learning based on multi-task deep learning, thereby realizing assessment of aesthetic quality of the natural image. The present application uses semantic information to assist learning of expressions of aesthetic characteristics so as to assess aesthetic quality more effectively, besides, the present application designs various multi-task deep learning network structures so as to effectively use the aesthetic and semantic information for obtaining highly accurate image aesthetic categorization. The present application can be applied to many fields relating to image aesthetic quality assessment, including image retrieval, photography and album management, etc.


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