A motion-flow-based fast video retrieval system
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
FPGA architecture for static background subtraction in real time
SBCCI '06 Proceedings of the 19th annual symposium on Integrated circuits and systems design
Evaluation of video news classification techniques for automatic content personalisation
International Journal of Advanced Media and Communication
Trajectory tree as an object-oriented hierarchical representation for video
IEEE Transactions on Circuits and Systems for Video Technology
Spatiotemporal region enhancement and merging for unsupervized object segmentation
Journal on Image and Video Processing
Video news classification for automatic content personalization: a genetic algorithm based approach
Proceedings of the 14th Brazilian Symposium on Multimedia and the Web
Taxonomy of directing semantics for film shot classification
IEEE Transactions on Circuits and Systems for Video Technology
On-line prediction of nonstationary variable-bit-rate video traffic
IEEE Transactions on Signal Processing
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
System Level Design and Implementation for Region-of-Interest Segmentation
Journal of Signal Processing Systems
Robust 2D moving object segmentation and tracking in video sequences
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
What can we learn from biological vision studies for human motion segmentation?
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Color recognition with compact color features
International Journal of Communication Systems
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A novel unsupervised video object segmentation algorithm is presented, aiming to segment a video sequence to objects: spatiotemporal regions representing a meaningful part of the sequence. The proposed algorithm consists of three stages: initial segmentation of the first frame using color, motion, and position information, based on a variant of the K-means-with-connectivity-constraint algorithm; a temporal tracking algorithm, using a Bayes classifier and rule-based processing to reassign changed pixels to existing regions and to efficiently handle the introduction of new regions; and a trajectory-based region merging procedure that employs the long-term trajectory of regions, rather than the motion at the frame level, so as to group them to objects with different motion. As shown by experimental evaluation, this scheme can efficiently segment video sequences with fast moving or newly appearing objects. A comparison with other methods shows segmentation results corresponding more accurately to the real objects appearing on the image sequence.