A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Learning words from sights and sounds: a computational model
Learning words from sights and sounds: a computational model
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combining Local and Global Image Features for Object Class Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Open-ended category learning for language acquisition
Connection Science - Language and Robots
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
Semantic Image Search and Subset Selection for Classifier Training in Object Recognition
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Semantic Image Search and Subset Selection for Classifier Training in Object Recognition
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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Different types of visual object categories can be found in real-world applications. Some categories are very heterogeneous in terms of local features (broad categories) while others are consistently characterized by some highly distinctive local features (narrow categories). The work described in this paper was motivated by the need to develop representations and categorization mechanisms that can be applied to domains involving different types of categories. A second concern of the paper is that these representations and mechanisms have potential for scaling up to large numbers of categories. The approach is based on combinining global shape descriptors with local features. A new shape representation is proposed. Two additional representations are used, one also capturing the object's shape and another based on sets of highly distinctive local features. Basic classifiers following the nearest-neighbor rule were implemented for each representation. A meta-level classifier, based on a voting strategy, was also implemented. The relevance of each representation and classifier to both broad and narrow categories is evaluated on two datasets with a combined total of 114 categories.