東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Fine-Grained Video Classification fo...

Shan, Junjie.

FindBook

Google Book

Amazon

博客來

Fine-Grained Video Classification for Rare Events.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fine-Grained Video Classification for Rare Events./
作者:	Shan, Junjie.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	85 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
Contained By:	Dissertations Abstracts International80-05B.
標題:	Information Technology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10979356
ISBN:	9780438688810

Fine-Grained Video Classification for Rare Events.
Shan, Junjie.

Fine-Grained Video Classification for Rare Events. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 85 p.

Source: Dissertations Abstracts International, Volume: 80-05, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.

This item must not be added to any third party search indexes.

Video analysis plays an important role in the field of computer vision and finds its application in many areas. Fine-grained event classification is one of the most challenging problems in video analysis due to subtle difference between classes and limited training examples, such as echocardiogram function prediction and social insect behavior classification. The difference between patterns of interest in these tasks is hard to perceive so we must rely on domain experts with professional skills to annotate the unlabeled videos. As a result, the data set of annotated videos is usually in small quantity or severely unbalanced. The performance of various traditional shallow learning methods is bounded by handcrafted feature extraction and data scarcity. Recently, the methods based on deep learning, such as convolutional neural network (CNN), have made substantial advancements in various vision tasks. They learn feature representation in a pure data-driven manner. In this dissertation, we propose a set of methods to address three fine-grained video classification problems for rare events. We first present an approach to classify fine-grained echocardiogram videos with subtle difference and limited training data using 3D CNN. Then, we investigate an autoencoder with 3D CNN structure and additional one-class support vector machine (OCSVM) layer to detect impaired heart videos using unbalanced echocardiogram dataset. Finally, we propose a pipeline to localize fine-grained pairwise ant behaviors, by generating behavior proposals from convolutional feature maps computed by 3D CNN.

ISBN: 9780438688810Subjects--Topical Terms:

1030799
Information Technology.

Fine-Grained Video Classification for Rare Events.
LDR:02757nmm a2200337 4500 001 2207839
005 20190923114240.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438688810
035 $a (MiAaPQ)AAI10979356
035 $a (MiAaPQ)uncc:11911
035 $a AAI10979356
040 $a MiAaPQ $c MiAaPQ
100 1 $a Shan, Junjie. $3 3434840
245 1 0 $a Fine-Grained Video Classification for Rare Events.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 85 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-05, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Shin, Min C.;Souvenir, Richard M.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.
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 Video analysis plays an important role in the field of computer vision and finds its application in many areas. Fine-grained event classification is one of the most challenging problems in video analysis due to subtle difference between classes and limited training examples, such as echocardiogram function prediction and social insect behavior classification. The difference between patterns of interest in these tasks is hard to perceive so we must rely on domain experts with professional skills to annotate the unlabeled videos. As a result, the data set of annotated videos is usually in small quantity or severely unbalanced. The performance of various traditional shallow learning methods is bounded by handcrafted feature extraction and data scarcity. Recently, the methods based on deep learning, such as convolutional neural network (CNN), have made substantial advancements in various vision tasks. They learn feature representation in a pure data-driven manner. In this dissertation, we propose a set of methods to address three fine-grained video classification problems for rare events. We first present an approach to classify fine-grained echocardiogram videos with subtle difference and limited training data using 3D CNN. Then, we investigate an autoencoder with 3D CNN structure and additional one-class support vector machine (OCSVM) layer to detect impaired heart videos using unbalanced echocardiogram dataset. Finally, we propose a pipeline to localize fine-grained pairwise ant behaviors, by generating behavior proposals from convolutional feature maps computed by 3D CNN.
590 $a School code: 0694.
650 4 $a Information Technology. $3 1030799
650 4 $a Computer science. $3 523869
690 $a 0489
690 $a 0984
710 2 $a The University of North Carolina at Charlotte. $b Computer Science. $3 3181506
773 0 $t Dissertations Abstracts International $g 80-05B.
790 $a 0694
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10979356