Effective multiple feature hashing for large-scale near-duplicate video retrieval

Song, Jingkuan, Yang, Yi, Huang, Zi, Shen, Heng Tao and Luo, Jiebo (2013) Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Transactions on Multimedia, 15 8: 1997-2008. doi:10.1109/TMM.2013.2271746

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Author Song, Jingkuan
Yang, Yi
Huang, Zi
Shen, Heng Tao
Luo, Jiebo
Title Effective multiple feature hashing for large-scale near-duplicate video retrieval
Journal name IEEE Transactions on Multimedia   Check publisher's open access policy
ISSN 1520-9210
Publication date 2013-07-03
Sub-type Article (original research)
DOI 10.1109/TMM.2013.2271746
Open Access Status Not yet assessed
Volume 15
Issue 8
Start page 1997
End page 2008
Total pages 12
Place of publication Piscataway, NJ, United States
Publisher IEEE
Language eng
Subject 1711 Signal Processing
2214 Media Technology
1706 Computer Science Applications
2208 Electrical and Electronic Engineering
Abstract Near-duplicate video retrieval (NDVR) has recently attracted much research attention due to the exponential growth of online videos. It has many applications, such as copyright protection, automatic video tagging and online video monitoring. Many 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. Moreover, 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 structural information of each individual feature and also globally considers the local structures for all the features to learn a group of hash functions to 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 collected from YouTube by ourselves. This dataset has been released (http://itee.uq.edu.au/shenht/UQ_VIDEO/). The experimental results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
Keyword Hashing
Manifold learning
Near-duplicate video retrieval
Video indexing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 56 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 71 times in Scopus Article | Citations
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Created: Fri, 22 Nov 2013, 19:50:52 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering