Bounded Coordinate System Indexing for Real-Time Video Clip Search
AuthorHuang, Z; Shen, HT; Shao, J; Zhou, X; Cui, B
Source TitleACM Transactions on Information Systems
PublisherASSOC COMPUTING MACHINERY
University of Melbourne Author/sSHAO, JIE
AffiliationComputer Science and Software Engineering
Document TypeJournal Article
CitationsHuang, Z., Shen, H. T., Shao, J., Zhou, X. & Cui, B. (2009). Bounded Coordinate System Indexing for Real-Time Video Clip Search. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 27 (3), https://doi.org/10.1145/1508850.1508855.
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<jats:p> Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant clips. Different from traditional long videos, a video clip is a short video often expressing a moment of significance. Due to the high complexity of video data, efficient video clip search from large databases turns out to be very challenging. We propose a novel video clip representation model called the <jats:italic>Bounded Coordinate System</jats:italic> (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip. It summarizes a video clip by a coordinate system, where each of its coordinate axes is identified by principal component analysis (PCA) and bounded by the range of data projections along the axis. The similarity measure of BCS considers the operations of translation, rotation, and scaling for coordinate system matching. Particularly, rotation and scaling reflect the difference of content tendencies. Compared with the quadratic time complexity of existing methods, the time complexity of measuring BCS similarity is linear. The compact video representation together with its linear similarity measure makes real-time search from video clip collections feasible. To further improve the retrieval efficiency for large video databases, a two-dimensional transformation method called <jats:italic>Bidistance Transformation</jats:italic> (BDT) is introduced to utilize a pair of optimal reference points with respect to bidirectional axes in BCS. Our extensive performance study on a large database of more than 30,000 video clips demonstrates that BCS achieves very high search accuracy according to human judgment. This indicates that content tendencies are important in determining the meanings of video clips and confirms that BCS can capture the inherent moment of video clip to some extent that better resembles human perception. In addition, BDT outperforms existing indexing methods greatly. Integration of the BCS model and BDT indexing can achieve real-time search from large video clip databases. </jats:p>
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