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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings./
作者:	Chen, Ming.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	113 p.
附註:	Source: Dissertations Abstracts International, Volume: 79-09, Section: B.
Contained By:	Dissertations Abstracts International79-09B.
標題:	Electrical engineering. -
電子資源:	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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