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:
Aug. 20, 2019

Filed:

Oct. 24, 2017
Applicant:

Beihang University, Beijing, CN;

Inventors:

Xiaowu Chen, Beijing, CN;

Changqun Xia, Beijing, CN;

Jia Li, Beijing, CN;

Qinping Zhao, Beijing, CN;

Assignee:

BEIHANG UNIVERSITY, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/46 (2006.01); G06T 7/194 (2017.01); G06T 7/70 (2017.01); G06T 7/80 (2017.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06K 9/4671 (2013.01); G06K 9/4638 (2013.01); G06K 9/6262 (2013.01); G06T 7/11 (2017.01); G06T 7/143 (2017.01); G06T 7/194 (2017.01); G06T 7/70 (2017.01); G06T 7/80 (2017.01); G06T 2207/10024 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20156 (2013.01);
Abstract

Provided is a method for salient object segmentation of an image by aggregating a multi-linear exemplar regressors, including: analyzing and summarizing visual attributes and features of a salient object and a non-salient object using background prior and constructing a quadratic optimization problem, calculating an initial saliency probability map, selecting a most trusted foreground and a background seed point, performing manifold preserving foreground propagation, generating a final foreground probability map; generating a candidate object set for the image via an objectness adopting proposal, using a shape feature, a foregroundness and an attention feature to characterize each candidate object, training the linear exemplar regressors for each training image to characterize a particular saliency pattern of the image; aggregating a plurality of linear exemplar regressors, calculating saliency values for the candidate object set of a test image, and forming an image salient object segmentation model capable of processing various complex scenarios.


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