Automated Lecture Video Segmentation: Facilitate Content Browsing and Retrieval

Persistent Link:
http://hdl.handle.net/10150/193843
Title:
Automated Lecture Video Segmentation: Facilitate Content Browsing and Retrieval
Author:
Lin, Ming
Issue Date:
2006
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
People often have difficulties finding 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.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.
Type:
text; Electronic Dissertation
Keywords:
Video segmentation; lecture video; dynamic segmentation; e-learning
Degree Name:
DMgt
Degree Level:
doctoral
Degree Program:
Management Information Systems; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Nunamaker, Jay F.
Committee Chair:
Nunamaker, Jay F.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleAutomated Lecture Video Segmentation: Facilitate Content Browsing and Retrievalen_US
dc.creatorLin, Mingen_US
dc.contributor.authorLin, Mingen_US
dc.date.issued2006en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractPeople often have difficulties finding 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.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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectVideo segmentationen_US
dc.subjectlecture videoen_US
dc.subjectdynamic segmentationen_US
dc.subjecte-learningen_US
thesis.degree.nameDMgten_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineManagement Information Systemsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorNunamaker, Jay F.en_US
dc.contributor.chairNunamaker, Jay F.en_US
dc.contributor.committeememberZhao, J. Leonen_US
dc.contributor.committeememberZeng, Daniel D.en_US
dc.identifier.proquest1730en_US
dc.identifier.oclc659747484en_US
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