Algorithms for clustering data
Algorithms for clustering data
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast Euclidean Distance Transform using a Graph-Search Algorithm
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Links between perceptrons, MLPs and SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Concept boundary detection for speeding up SVMs
ICML '06 Proceedings of the 23rd international conference on Machine learning
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multiclass reduced-set support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rotation-invariant texture recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Fuzzy Classifier Design
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
A New Variant of the Optimum-Path Forest Classifier
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Novel Approaches for Exclusive and Continuous Fingerprint Classification
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A Learning Algorithm for the Optimum-Path Forest Classifier
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
On the Training Patterns Pruning for Optimum-Path Forest
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Synergistic arc-weight estimation for interactive image segmentation using graphs
Computer Vision and Image Understanding
Fast and accurate holistic face recognition using optimum-path forest
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Fast interactive segmentation of natural images using the image foresting transform
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Spoken emotion recognition through optimum-path forest classification using glottal features
Computer Speech and Language
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We present an approach for supervised pattern recognition based on combinatorial analysis of optimum paths from key samples (prototypes), which creates a discrete optimal partition of the feature space such that any unknown sample can be classified according to this partition. A training set is interpreted as a complete graph with at least one prototype in each class. They compete among themselves and each prototype defines an optimum-path tree, whose nodes are the samples more strongly connected to it than to any other. The result is an optimumpath forest in the training set. A test sample is assigned to the class of the prototype which offers it the optimum path in the forest. The classifier is designed to achieve zero classification errors in the training set, without over-fitting, and to learn from its errors. A comparison with several datasets shows the advantages of the method in accuracy and efficiency with respect to support vector machines.