Engineering Applications of Artificial Intelligence
Improving the accuracy of the optimum-path forest supervised classifier for large datasets
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Efficient supervised optimum-path forest classification for large datasets
Pattern Recognition
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Computers and Electrical Engineering
IFTrace: Video segmentation of deformable objects using the Image Foresting Transform
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Aquatic weed automatic classification using machine learning techniques
Computers and Electronics in Agriculture
An Optimum-Path Forest framework for intrusion detection in computer networks
Engineering Applications of Artificial Intelligence
Brain tissue MR-image segmentation via optimum-path forest clustering
Computer Vision and Image Understanding
Expert Systems with Applications: An International Journal
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A relevance feedback approach for the author name disambiguation problem
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest
Expert Systems with Applications: An International Journal
A comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers
Pattern Recognition Letters
Pattern Recognition Letters
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We present a supervised classification method which represents each class by one or more optimum-path trees rooted at some key samples, called prototypes. The training samples are nodes of a complete graph, whose arcs are weighted by the distances between the feature vectors of their nodes. Prototypes are identified in all classes and the minimization of a connectivity function by dynamic programming assigns to each training sample a minimum-cost path from its most strongly connected prototype. This competition among prototypes partitions the graph into an optimum-path forest rooted at them. The class of the samples in an optimum-path tree is assumed to be the same of its root. A test sample is classified similarly, by identifying which tree would contain it, if the sample were part of the training set. By choice of the graph model and connectivity function, one can devise other optimum-path forest classifiers. We present one of them, which is fast, simple, multiclass, parameter independent, does not make any assumption about the shapes of the classes, and can handle some degree of overlapping between classes. We also propose a general algorithm to learn from errors on an evaluation set without increasing the training set, and show the advantages of our method with respect to SVM, ANN-MLP, and k-NN classifiers in several experiments with datasets of various types. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 120–131, 2009. A preliminary version of the paper was presented at the 12th International Workshop on Combinatorial Image Analysis (Papa et al.,2008a).