Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Error Estimators for Pruning Regression Trees
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Generalized Multiclass AdaBoost and Its Applications to Multimedia Classification
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
International Journal of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Elgasir: an algorithm for creating fuzzy regression trees
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A coarse-to-fine taxonomy of constellations for fast multi-class object detection
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
Journal of Systems Architecture: the EUROMICRO Journal
Hierarchical kernel-based rotation and scale invariant similarity
Pattern Recognition
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We are often faced with the problem of distinguishing between visually similar objects that share the same general appearance characteristics. As opposed to object categorization, this task is focused on capturing fine image differences in a pose-dependent fashion. Our research addresses this particular family of problems and is centered around the concept of learning from example pairs. Formally, we construct a parameterized visual similarity function optimally separating the pairs of images that depict the objects of the same class or identity from the pairs representing different object classes/identities. It combines various image distances that are quantified by comparing local descriptor responses at the corresponding locations in both paired images. To find the best combinations, we train ensembles of so-called Kernel Regression Trees which model the desired similarity function as a hierarchy of fuzzy decision stumps. The obtained function is then used within a k-NN-like framework to address complex multi-classification problems. Through the experiments with several image datasets we demonstrate the numerous advantages of the proposed approach: high classification accuracy, scalability, ease of interpretation and the independence of the feature representation.