Multiple feature hashing for real-time large scale near-duplicate video retrieval

Song, J; Yang, Y; Huang, Z; Shen, HT; Hong, R

Multiple feature hashing for real-time large scale near-duplicate video retrieval

Song, J Yang, Y

Huang, Z Shen, HT Hong, R

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Publication Type:: Conference Proceeding
Citation:: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 423 - 432
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Song, J	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Shen, HT	en_US
dc.contributor.author	Hong, R	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 423 - 432	en_US
dc.identifier.isbn	9781450306164	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119944
dc.description.abstract	Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Besides, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structure information of each individual feature and also globally consider the local structures for all the features to learn a group of hash functions which map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos, which was collected from YouTube by ourselves. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency. © 2011 ACM.	en_US
dc.relation.ispartof	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops	en_US
dc.relation.isbasedon	10.1145/2072298.2072354	en_US
dc.title	Multiple feature hashing for real-time large scale near-duplicate video retrieval	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Besides, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structure information of each individual feature and also globally consider the local structures for all the features to learn a group of hash functions which map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos, which was collected from YouTube by ourselves. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency. © 2011 ACM.

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http://hdl.handle.net/10453/119944