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

Automated lecture video segmentation...

Lin, Ming.

FindBook

Google Book

Amazon

博客來

Automated lecture video segmentation: Facilitate content browsing and retrieval.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated lecture video segmentation: Facilitate content browsing and retrieval./
作者:	Lin, Ming.
面頁冊數:	155 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-05, Section: A, page: 1574.
Contained By:	Dissertation Abstracts International67-05A.
標題:	Information Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3218225
ISBN:	9780542675171

Automated lecture video segmentation: Facilitate content browsing and retrieval.
Lin, Ming.

Automated lecture video segmentation: Facilitate content browsing and retrieval. - 155 p.

Source: Dissertation Abstracts International, Volume: 67-05, Section: A, page: 1574.

Thesis (Ph.D.)--The University of Arizona, 2006.

People often have difficulties fording specific information in video because of its linear and unstructured nature. Segmenting long videos into small clips by topics and providing browsing and search functionalities is beneficial for information searching. However, manual segmentation is labor intensive and existing automated segmentation methods are not effective for plenty of amateur made and unedited lecture videos. The objectives of this dissertation are to develop (1) automated segmentation algorithms to extract the topic structure of a lecture video, and (2) retrieval algorithms to identify the relevant video segments for user queries.

ISBN: 9780542675171Subjects--Topical Terms:

1017528
Information Science.

Automated lecture video segmentation: Facilitate content browsing and retrieval.
LDR:02465nmm 2200289 4500 001 1828330
005 20071112135313.5
008 130610s2006 eng d
020 $a 9780542675171
035 $a (UMI)AAI3218225
035 $a AAI3218225
040 $a UMI $c UMI
100 1 $a Lin, Ming. $3 1908727
245 1 0 $a Automated lecture video segmentation: Facilitate content browsing and retrieval.
300 $a 155 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-05, Section: A, page: 1574.
500 $a Adviser: Jay F. Nunamaker.
502 $a Thesis (Ph.D.)--The University of Arizona, 2006.
520 $a People often have difficulties fording specific information in video because of its linear and unstructured nature. Segmenting long videos into small clips by topics and providing browsing and search functionalities is beneficial for information searching. However, manual segmentation is labor intensive and existing automated segmentation methods are not effective for plenty of amateur made and unedited lecture videos. The objectives of this dissertation are to develop (1) automated segmentation algorithms to extract the topic structure of a lecture video, and (2) retrieval algorithms to identify the relevant video segments for user queries.
520 $a Based on an extensive literature review, existing segmentation features and approaches are summarized and research challenges and questions are presented. Manual segmentation studies are conducted to understand the content structure of a lecture video and a set of potential segmentation features and methods are extracted to facilitate the design of automated segmentation approaches. Two static algorithms are developed to segment a lecture video into a list of topics. Features from multimodalities and various knowledge sources (e.g. electronic slides) are used in the segmentation algorithms. A dynamic segmentation method is also developed to retrieve relevant video segments of appropriate sizes based on the questions asked by users. A series of evaluation studies are conducted and results are presented to demonstrate the effectiveness and usefulness of the automated segmentation approaches.
590 $a School code: 0009.
650 4 $a Information Science. $3 1017528
650 4 $a Computer Science. $3 626642
690 $a 0723
690 $a 0984
710 2 0 $a The University of Arizona. $3 1017508
773 0 $t Dissertation Abstracts International $g 67-05A.
790 1 0 $a Nunamaker, Jay F., $e advisor
790 $a 0009
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3218225