Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple binary decision tree classifiers
Pattern Recognition
Nonparametric classification using matched binary decision trees
Pattern Recognition Letters
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern classification with compact distribution maps
Computer Vision and Image Understanding
Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Applying A Hybrid Method To Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Language Identification of Character Images Using Machine Learning Techniques
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Building Projectable Classifiers of Arbitrary Complexity
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast block matching algorithm based on the winner-update strategy
IEEE Transactions on Image Processing
A fast multiresolution feature matching algorithm for exhaustive search in large image databases
IEEE Transactions on Circuits and Systems for Video Technology
Techniques for solving the large-scale classification problem in Chinese handwriting recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Object recognition using discriminative parts
Computer Vision and Image Understanding
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We propose two methods to accelerate the matching of an unknown object with known objects, all of which are expressed as feature vectors. The acceleration becomes necessary when the population of known objects is large and a great deal of time would be required to match all of them. Our proposed methods are multiple decision trees and sub-vector matching, both of which use a learning procedure to estimate the optimal values of certain parameters. Online matching with a combination of the two methods is then performed, whereby candidates are matched rapidly without sacrificing the test accuracy. The process is demonstrated by experiments in which we apply the proposed methods to handwriting recognition and language identification. The speed-up factor of our approach is dramatic compared with an alternative approach that eliminates candidates in a deterministic fashion.