Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Image and Video Compression for Multimedia Engineering
Image and Video Compression for Multimedia Engineering
Video Clustering Using SuperHistograms in Large Archives
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Motion-Based Semantic Event Detection for Video Content Description in MPEG-7
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Motion Activity Based Semantic Video Similarity Retrieval
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Motion-Location Based Indexing Method for Retrieving MPEG Videos
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
Efficient matching and clustering of video shots
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Content-based retrieval of video shot using the-improved nearest feature line method
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual motion descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Content-aware video adaptation under low-bitrate constraint
EURASIP Journal on Advances in Signal Processing
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation
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Due to the tremendous growth in the number of digital videos, the development of video retrieval algorithms that can perform efficient and effective retrieval task is indispensable. In this paper, we propose a high-level motion activity descriptor, object-based transformed 2D-histogram (T2D-histogram), which exploits both spatial and temporal features to characterize video sequences in a semantics-based manner. The discrete cosine transform (DCT) is applied to convert the object-based 2D-histogram sequences from the time domain to the frequency domain. Using this transform, the original high-dimensional time domain features used to represent successive frames are significantly reduced to a set of low-dimensional features in frequency domain. The energy concentration property of DCT allows us to use only a few DCT coefficients to effectively capture the variations of moving objects. Having the efficient scheme for video representation, one can perform video retrieval in an accurate and efficient way.