C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-resolution image registration using multi-class Hausdorff fraction
Pattern Recognition Letters
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Robust Face Detection at Video Frame Rate Based on Edge Orientation Features
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
A Two-Stage Classifier for Broken and Blurred Digits in Forms
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition
Journal of Cognitive Neuroscience
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Adaptive mixtures of local experts
Neural Computation
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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We address the problem of computationally efficient visual classification of objects, and propose a system for solving multi-class problems in domains that have inherent hierarchic structure, such as subclass-superclass-relationships based on visual similarity. Class relationships are used at runtime to select the computationally simplest feature space that allows classification at high level of confidence for each example view. Classification accuracies can then be further improved using rank-order voting over multiple views. Our experimental results show that our system compares favorably to previously published results using a demanding benchmark. The results support the hypothesis that class hierarchies based on visual similarities are feasible and useful in controlling the accuracy vs. speed tradeoffs in classification.