On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation
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
A class of generalized median contour problem with exact solution
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic selection of parameters for vessel/neurite segmentation algorithms
IEEE Transactions on Image Processing
An Instability Problem of Region Growing Segmentation Algorithms and Its Set Median Solution
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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
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Unsupervised image segmentation is of essential relevance for many computer vision applications and remains a difficult task despite of decades of intensive research. In particular, the parameter selection problem has not received the due attention in the past. Researchers typically claim to have empirically fixed the parameter values or train in advance based on manual ground truth. These approaches are not optimal and lack an adaptive behavior in dealing with a particular image. In this work we adopt the ensemble combination principle to solve the parameter selection problem in image segmentation. It explores the parameter space without the need of ground truth. The experimental results including a comparison with ground truth based training demonstrate the effectiveness of our framework.