Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
SOM Ensemble-Based Image Segmentation
Neural Processing Letters
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-based averaging for combination of multiple segmentations
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Efficient combination of probabilistic sampling approximations for robust image segmentation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensemble Combination for Solving the Parameter Selection Problem in Image Segmentation
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper we propose an algorithm for combining multiple image segmentations to achieve a final improved segmentation. In contrast to previous works we consider the most general class of segmentation combination, i.e. each input segmentation can have an arbitrary number of regions. Our approach is based on a random walker segmentation algorithm which is able to provide high-quality segmentation starting from manually specified seeds. We automatically generate such seeds from an input segmentation ensemble. Two applications scenarios are considered in this work: Exploring the parameter space and segmenter combination. Extensive tests on 300 images with manual segmentation ground truth have been conducted and our results clearly show the effectiveness of our approach in both situations.