An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Image segmentation from consensus information
Computer Vision and Image Understanding
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Based on Cluster Ensemble
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Ensemble clustering using semidefinite programming with applications
Machine Learning
Weighted partition consensus via kernels
Pattern Recognition
Efficient combination of probabilistic sampling approximations for robust image segmentation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
A clustering-based ensemble technique for shape decomposition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
An indication of unification for different clustering approaches
Pattern Recognition
Hi-index | 0.00 |
A new framework for adapting common ensemble clustering methods to solve the image segmentation combination problem is presented. The framework is applied to the parameter selection problem in image segmentation and compared with supervised parameter learning. We quantitatively evaluate 9 ensemble clustering methods requiring a known number of clusters and 4 with adaptive estimation of the number of clusters. Experimental results explore the capabilities of the proposed framework. It is shown that the ensemble clustering approach yields results close to the supervised learning, but without any ground truth information.