Semantic context transfer across heterogeneous sources for domain adaptive video search

Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propos...

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Main Authors: JIANG, Yu-Gang, NGO, Chong-wah, CHANG, Shih-Fu
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Language:English
Published: Institutional Knowledge at Singapore Management University 2009
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Online Access:https://ink.library.smu.edu.sg/sis_research/6527
https://ink.library.smu.edu.sg/context/sis_research/article/7530/viewcontent/1631272.1631296.pdf
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spelling sg-smu-ink.sis_research-75302022-01-10T03:49:12Z Semantic context transfer across heterogeneous sources for domain adaptive video search JIANG, Yu-Gang NGO, Chong-wah CHANG, Shih-Fu Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual corpus. Starting from an initial detector set determined by FCS, our approach novelly transfers semantic context learned from test data domain to adaptively refine the query-detector similarity. The semantic context transfer process provides an effective means to cope with the domain shift between external knowledge source (e.g., Flickr context) and test data, which is a critical issue in video search. To the best of our knowledge, this work represents the first research aiming to tackle the challenging issue of domain change in video search. Extensive experiments on 120 textual queries over TRECVID 2005–2008 data sets demonstrate the effectiveness of semantic context transfer for domain adaptive video search. Results also show that the FCS is suitable for measuring query-detector similarity, producing better performance to various other popular measures 2009-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6527 info:doi/10.1145/1631272.1631296 https://ink.library.smu.edu.sg/context/sis_research/article/7530/viewcontent/1631272.1631296.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 Domain adaptive video search Flickr context similarity Heterogeneous sources Semantic context transfer 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 Domain adaptive video search
Flickr context similarity
Heterogeneous sources
Semantic context transfer
Data Storage Systems
Graphics and Human Computer Interfaces
spellingShingle Domain adaptive video search
Flickr context similarity
Heterogeneous sources
Semantic context transfer
Data Storage Systems
Graphics and Human Computer Interfaces
JIANG, Yu-Gang
NGO, Chong-wah
CHANG, Shih-Fu
Semantic context transfer across heterogeneous sources for domain adaptive video search
description Automatic video search based on semantic concept detectors has recently received significant attention. Since the number of available detectors is much smaller than the size of human vocabulary, one major challenge is to select appropriate detectors to response user queries. In this paper, we propose a novel approach that leverages heterogeneous knowledge sources for domain adaptive video search. First, instead of utilizing WordNet as most existing works, we exploit the context information associated with Flickr images to estimate query-detector similarity. The resulting measurement, named Flickr context similarity (FCS), reflects the co-occurrence statistics of words in image context rather than textual corpus. Starting from an initial detector set determined by FCS, our approach novelly transfers semantic context learned from test data domain to adaptively refine the query-detector similarity. The semantic context transfer process provides an effective means to cope with the domain shift between external knowledge source (e.g., Flickr context) and test data, which is a critical issue in video search. To the best of our knowledge, this work represents the first research aiming to tackle the challenging issue of domain change in video search. Extensive experiments on 120 textual queries over TRECVID 2005–2008 data sets demonstrate the effectiveness of semantic context transfer for domain adaptive video search. Results also show that the FCS is suitable for measuring query-detector similarity, producing better performance to various other popular measures
format text
author JIANG, Yu-Gang
NGO, Chong-wah
CHANG, Shih-Fu
author_facet JIANG, Yu-Gang
NGO, Chong-wah
CHANG, Shih-Fu
author_sort JIANG, Yu-Gang
title Semantic context transfer across heterogeneous sources for domain adaptive video search
title_short Semantic context transfer across heterogeneous sources for domain adaptive video search
title_full Semantic context transfer across heterogeneous sources for domain adaptive video search
title_fullStr Semantic context transfer across heterogeneous sources for domain adaptive video search
title_full_unstemmed Semantic context transfer across heterogeneous sources for domain adaptive video search
title_sort semantic context transfer across heterogeneous sources for domain adaptive video search
publisher Institutional Knowledge at Singapore Management University
publishDate 2009
url https://ink.library.smu.edu.sg/sis_research/6527
https://ink.library.smu.edu.sg/context/sis_research/article/7530/viewcontent/1631272.1631296.pdf
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