Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Automatic moving object extraction for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Towards Fully Automatic Image Segmentation Evaluation
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
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Motion-based segmentation is traditionally used for video object extraction. Objects are detected as groups of significant moving regions and tracked through the sequence. However, this approach presents difficulties for video shots that contain both static and dynamic moments, and detection is prone to fail in absence of motion. In addition, retrieval of static contents is needed for high-level descriptions. In this paper, we present a new graph-based approach to extract spatio-temporal regions. The method performs iteratively on pairs of frames through a hierarchical merging process. Spatial merging is first performed to build spatial atomic regions, based on color similarities. Then, we propose a new matching procedure for the temporal grouping of both static and moving regions. A feature point tracking stage allows to create dynamic temporal edges between frames and group strongly connected regions. Space-time constraints are then applied to merge the main static regions and a region graph matching stage completes the procedure to reach high temporal coherence. Finally, we show the potential of our method for the segmentation of real moving video sequences.