Quantitative measures of change based on feature organization: eigenvalues and eigenvectors
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised Texture Segmentation by Dominant Sets and Game Dynamics
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fast communication: Dominant sets clustering for image retrieval
Signal Processing
Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernels
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Video scene detection using graph-based representations
Image Communication
Dominant sets based movie scene detection
Signal Processing
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Pairwise data clustering techniques are gaining increasing popularity over traditional, feature-based central grouping techniques. These approaches have proven very powerful when applied to image-segmentation problems. However, they are computationally too demanding to be applied to video-segmentaton problems due to their scaling behavior with the quantity of data. On a dataset containing N examples, the number of potential comparisons scales with O(N2), thereby rendering the approaches unfeasible for problems involving very large data sets. It is therefore of primary importance to develop strategies to reduce the number of comparisons required by subsampling the data and extending the grouping to out-of-sample points after the clustering process has taken place. In this paper we present an approach to out-of-sample clustering based on the dominant set framework [10] and apply it to video segmentation. The method is compared against two recent pairwise clustering algorithms which provide out-of-sample extensions: the Nyström method [3], and the minimal-shift embedding approach [14]. Our results show that our approach performs comparably against the competition in terms of quality of the segmentation, being, however, much faster.