Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Feature Weighting in k-Means Clustering
Machine Learning
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unifying Approach to Hard and Probabilistic Clustering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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The ability of a segmentation algorithm to uncover an interesting partition of an image critically depends on its capability to utilize and combine all available, relevant information. This paper investigates a method to automatically weigh different data sources, such that a meaningful segmentation is uncovered. Different sources of information naturally arise in image segmentation, e.g. as intensity measurements, local texture information or edge maps. The data fusion is controlled by a regularization mechanism, favoring sparse solutions. Regularization parameters as well as the clustering complexity are determined by the concept of cluster stability yielding maximally reproducible segmentations. Experiments on the Berkeley segmentation database show that our segmentation approach outperforms competing segmentation algorithms and performs comparably to supervised boundary detectors.