Issue No. 10 - October (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.171
Xiangmin Zhou , Canberra ICT Center, CSIRO, Australia
Xiaofang Zhou , The University of Queensland, Brisbane
Lei Chen , Hong Kong University of Science and Technology, Hong Kong
Yanfeng Shu , Tasmanian ICT Center, CSIRO, Australia
Athman Bouguettaya , Canberra ICT Center, CSIRO, Australia
John A. Taylor , Canberra CMIS Center, CSIRO, Australia
Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.
Video detection, subspace symbolization, variable length partition, query optimization.
L. Chen, X. Zhou, J. A. Taylor, A. Bouguettaya, Y. Shu and X. Zhou, "Adaptive Subspace Symbolization for Content-Based Video Detection," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1372-1387, 2009.