A gram-based string paradigm for efficient video subsequence search

Huang, Zi, Liu, Jiajun, Cui, Bin and Du, Xiaoyong (2013) A gram-based string paradigm for efficient video subsequence search. IEEE Transactions On Multimedia, 15 3: 608-620. doi:10.1109/TMM.2012.2236307

Author Huang, Zi
Liu, Jiajun
Cui, Bin
Du, Xiaoyong
Title A gram-based string paradigm for efficient video subsequence search
Journal name IEEE Transactions On Multimedia   Check publisher's open access policy
ISSN 1520-9210
Publication date 2013-04
Year available 2012
Sub-type Article (original research)
DOI 10.1109/TMM.2012.2236307
Volume 15
Issue 3
Start page 608
End page 620
Total pages 13
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2014
Language eng
Abstract The unprecedented increase in the generation and dissemination of video data has created an urgent demand for the large-scale video content management system to quickly retrieve videos of users' interests. Traditionally, video sequence data are managed by high-dimensional indexing structures, most of which suffer from the well-known “curse of dimensionality” and lack of support of subsequence retrieval. Inspired by the high efficiency of string indexing methods, in this paper, we present a string paradigm called VideoGram for large-scale video sequence indexing to achieve fast similarity search. In VideoGram, the feature space is modeled as a set of visual words. Each database video sequence is mapped into a string. A gram-based indexing structure is then built to tackle the effect of the “curse of dimensionality” and support video subsequence matching. Given a high-dimensional query video sequence, retrieval is performed by transforming the query into a string and then searching the matched strings from the index structure. By doing so, expensive high-dimensional similarity computations can be completely avoided. An efficient sequence search algorithm with upper bound pruning power is also presented. We conduct an extensive performance study on real-life video collections to validate the novelties of our proposal.
Keyword High-dimensional indexing
Sequence indexing
Similarity search
Video subsequence search
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 24 December 2012.

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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