Elements of information theory
Elements of information theory
Computing spatiotemporal relations for dynamic perceptual organization
CVGIP: Image Understanding
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Segmentation by MAP Labeling of Watershed Segments
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
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ASSET-2: Real-Time Motion Segmentation and Shape Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Tracking 2D structures using perceptual organizational principles
ISCV '95 Proceedings of the International Symposium on Computer Vision
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Retrieval Based on Dynamics of Color Flows
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Regression based Bandwidth Selection for Segmentation using Parzen Windows
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probabilistic Space-Time Video Modeling via Piecewise GMM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical foundations for experiential systems design
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Representing moving images with layers
IEEE Transactions on Image Processing
Unsupervised video segmentation based on watersheds and temporal tracking
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
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
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Unsupervised clustering is an important tool to analyze video data. Selection of an appropriate clustering scheme is governed by the suitability of the clusters it produces. It is difficult to formulate cluster suitability criteria for a domain where different feature attributes have different meanings. We propose a novel clustering strategy, tailored towards the specific requirements of clustering in video data. Our clustering methodology decouples clustering along different feature components. Our scheme chooses the clustering model so as to meet the requirements of clustering in video data. The clusters obtained from our scheme reasonably model the homogeneous color regions in a video scene in both space and time. The space-time clusters obtained by our clustering methodology can be subsequently grouped together to compose meaningful objects. Experimental comparison of our results with existing clustering techniques clearly show that our scheme takes care of many of the problems with traditional clustering schemes applied to the heterogeneous feature space of video.