On the annotation of web videos by efficient near-duplicate search

With the proliferation of Web 2.0 applications, usersupplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large numbe...

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Main Authors: ZHAO, Wan-Lei, WU, Xiao, NGO, Chong-wah
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/sis_research/6336
https://ink.library.smu.edu.sg/context/sis_research/article/7339/viewcontent/10.1.1.330.9344.pdf
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spelling sg-smu-ink.sis_research-73392021-11-23T04:32:16Z On the annotation of web videos by efficient near-duplicate search ZHAO, Wan-Lei WU, Xiao NGO, Chong-wah With the proliferation of Web 2.0 applications, usersupplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues for annotating query videos. First, we provide a novel solution for fast near-duplicate video retrieval. Second, based on the outcome of near-duplicate search, we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online. Experiments on cross sources (annotating Google videos and Yahoo! videos using YouTube videos) and cross time periods (annotating YouTube videos using historical data) show the effectiveness and efficiency of the proposed classifier-free approach for web video tag annotation. 2010-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6336 info:doi/10.1109/TMM.2010.2050651 https://ink.library.smu.edu.sg/context/sis_research/article/7339/viewcontent/10.1.1.330.9344.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data-driven near-duplicate video search video annotation web video Data Storage Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data-driven
near-duplicate video search
video annotation
web video
Data Storage Systems
Graphics and Human Computer Interfaces
spellingShingle Data-driven
near-duplicate video search
video annotation
web video
Data Storage Systems
Graphics and Human Computer Interfaces
ZHAO, Wan-Lei
WU, Xiao
NGO, Chong-wah
On the annotation of web videos by efficient near-duplicate search
description With the proliferation of Web 2.0 applications, usersupplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues for annotating query videos. First, we provide a novel solution for fast near-duplicate video retrieval. Second, based on the outcome of near-duplicate search, we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online. Experiments on cross sources (annotating Google videos and Yahoo! videos using YouTube videos) and cross time periods (annotating YouTube videos using historical data) show the effectiveness and efficiency of the proposed classifier-free approach for web video tag annotation.
format text
author ZHAO, Wan-Lei
WU, Xiao
NGO, Chong-wah
author_facet ZHAO, Wan-Lei
WU, Xiao
NGO, Chong-wah
author_sort ZHAO, Wan-Lei
title On the annotation of web videos by efficient near-duplicate search
title_short On the annotation of web videos by efficient near-duplicate search
title_full On the annotation of web videos by efficient near-duplicate search
title_fullStr On the annotation of web videos by efficient near-duplicate search
title_full_unstemmed On the annotation of web videos by efficient near-duplicate search
title_sort on the annotation of web videos by efficient near-duplicate search
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/6336
https://ink.library.smu.edu.sg/context/sis_research/article/7339/viewcontent/10.1.1.330.9344.pdf
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