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Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings./
Author:	Chen, Ming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 79-09, Section: B.
Contained By:	Dissertations Abstracts International79-09B.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248551
ISBN:	9780355592436

Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings.
Chen, Ming.

Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 113 p.

Source: Dissertations Abstracts International, Volume: 79-09, Section: B.

Thesis (Ph.D.)--Purdue University, 2016.

This item is not available from ProQuest Dissertations & Theses.

Understanding the visual content of images is one of the most important topics in computer vision. Many researchers have tried to teach the machine to see and perceive like human. In this dissertation, we develop several new approaches for image understanding with applications to affective computing, and person detection and recognition. Our proposed method applied to fashion photo analysis can understand the aesthetic quality of photos. Further, a bilinear model that takes into account the relative confidence of region proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle various pose, viewpoints, and lighting conditions. The system is then put into several real scenarios that has different amount of labeled data. Our algorithm that utilizes unlabeled data reduces the effort needed for data annotation while achieving similar results as with labeled data.

ISBN: 9780355592436Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Image understanding

Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings.
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245 1 0 $a Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 113 p.
500 $a Source: Dissertations Abstracts International, Volume: 79-09, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Allebach, Jan P.
502 $a Thesis (Ph.D.)--Purdue University, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Understanding the visual content of images is one of the most important topics in computer vision. Many researchers have tried to teach the machine to see and perceive like human. In this dissertation, we develop several new approaches for image understanding with applications to affective computing, and person detection and recognition. Our proposed method applied to fashion photo analysis can understand the aesthetic quality of photos. Further, a bilinear model that takes into account the relative confidence of region proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle various pose, viewpoints, and lighting conditions. The system is then put into several real scenarios that has different amount of labeled data. Our algorithm that utilizes unlabeled data reduces the effort needed for data annotation while achieving similar results as with labeled data.
590 $a School code: 0183.
650 4 $a Electrical engineering. $3 649834
653 $a Image understanding
653 $a Person detection
653 $a Person recognition
653 $a Photo aesthetics
690 $a 0544
710 2 $a Purdue University. $b Electrical and Computer Engineering. $3 1018497
773 0 $t Dissertations Abstracts International $g 79-09B.
790 $a 0183
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248551