Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Class-Specific, Top-Down Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Object Tracking Using Adaptive Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Spatio-Temporal Robust Motion Estimation and Segmentation
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
A continuous probabilistic framework for image matching
Computer Vision and Image Understanding
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-dependent segmentation and matching in image databases
Computer Vision and Image Understanding
Learning Spatial Context from Tracking using Penalised Likelihoods
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
NeTra-V: toward an object-based video representation
IEEE Transactions on Circuits and Systems for Video Technology
Combination of accumulated motion and color segmentation for human activity analysis
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Automatic colour-texture image segmentation using active contours
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera
International Journal of Computer Vision
Robust Motion Detection via the Fuzzy Fusion of 6D Feature Space Decompositions
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Context-free detection of events
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Step-by-step description of lateral interaction in accumulative computation
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Compositional object recognition, segmentation, and tracking in video
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Bayesian order-adaptive clustering for video segmentation
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Rough set based image segmentation of video sequences
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data.