Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Monte-Carlo Algorithms for finding low-rank approximations
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Learning and Inferring Image Segmentations using the GBP Typical Cut Algorithm
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Multiple Segmentations by a Random Walker Approach
Proceedings of the 30th DAGM symposium on Pattern Recognition
Image segmentation fusion using general ensemble clustering methods
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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Methods based on pairwise similarity relations have been successfully applied to unsupervised image segmentation problems. One major drawback of such approaches is their computational demand which scales quadratically with the number of pixels. Adaptations to increase the efficiency have been presented, but the quality of the results obtained with those techniques tends to decrease. The contribution of this work is to address this tradeoff for a recent convex relaxation approach for image partitioning. We propose a combination of two techniques that results in a method which is both efficient and yields robust segmentations. The main idea is to use a probabilistic sampling method in a first step to obtain a fast segmentation of the image by approximating the solution of the convex relaxation. Repeating this process several times for different samplings, we obtain multiple different partitionings of the same image. In the second step we combine these segmentations by using a meta-clustering algorithm, which gives a robust final result that does not critically depend on the selected sample points.