Correlation-based retrieval for heavily changed near-duplicate videos

Liu, Jiajun, Huang, Zi, Shen, Heng Tao and Cui, Bin (2011) Correlation-based retrieval for heavily changed near-duplicate videos. ACM Transactions on Information Systems, 29 4: 21.1-21.25. doi:10.1145/2037661.2037666

Author Liu, Jiajun
Huang, Zi
Shen, Heng Tao
Cui, Bin
Title Correlation-based retrieval for heavily changed near-duplicate videos
Journal name ACM Transactions on Information Systems   Check publisher's open access policy
ISSN 1046-8188
Publication date 2011-12
Sub-type Article (original research)
DOI 10.1145/2037661.2037666
Volume 29
Issue 4
Start page 21.1
End page 21.25
Total pages 25
Place of publication New York, NY, United States
Publisher Association for Computing Machinery
Collection year 2012
Language eng
Abstract The unprecedented and ever-growing number of Web videos nowadays leads to the massive existence of near-duplicate videos. Very often, some near-duplicate videos exhibit great content changes, while the user perceives little information change, for example, color features change significantly when transforming a color video with a blue filter. These feature changes contribute to low-level video similarity computations, making conventional similarity-based near-duplicate video retrieval techniques incapable of accurately capturing the implicit relationship between two near-duplicate videos with fairly large content modifications. In this paper, we introduce a new dimension for near-duplicate video retrieval. Different from existing near-duplicate video retrieval approaches which are based on video-content similarity, we explore the correlation between two videos. The intuition is that near-duplicate videos should preserve strong information correlation in spite of intensive content changes. More effective retrieval with stronger tolerance is achieved by replacing video-content similarity measures with information correlation analysis. Theoretical justification and experimental results prove the effectiveness of correlation-based near-duplicate retrieval.
Keyword Design
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article no. 21

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 16 Oct 2011, 14:52:23 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering