Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A fast fixed-point algorithm for independent component analysis
Neural Computation
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
A non-parametric multi-scale statistical model for natural images
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Watershed-based segmentation and region merging
Computer Vision and Image Understanding
Independent component analysis: algorithms and applications
Neural Networks
Randomizing Outputs to Increase Prediction Accuracy
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Marker Extraction for Color Watershed in Segmenting Microscopic Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Issues in stacked generalization
Journal of Artificial Intelligence Research
Switching class labels to generate classification ensembles
Pattern Recognition
On deriving the second-stage training set for trainable combiners
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
IEEE Transactions on Image Processing
Classification-Driven Watershed Segmentation
IEEE Transactions on Image Processing
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Marker-driven watershed segmentation attempts to extract seeds that indicate the presence of objects within an image. These markers are subsequently used to enforce regional minima within a topological surface used by the watershed algorithm. The classification-driven watershed segmentation (CDWS) algorithm improved the production of markers and topological surface by employing two machine-learned pixel classifiers. The probability maps produced by the two classifiers were utilized for creating markers, object boundaries, and the topological surface. This paper extends the CDWS algorithm by (i) enabling automated feature extraction via independent components analysis and (ii) improving the segmentation accuracy by introducing heterogeneous stacking. Heterogeneous stacking, an extension of stacked generalization for object delineation, improves pixel labeling and segmentation by training base classifiers on multiple target concepts extracted from the original ground truth, which are subsequently fused by the second set of classifiers. Experimental results demonstrate the effectiveness of the proposed system on real world images, and indicate significant improvement in segmentation quality over the base system.