The nature of statistical learning theory
The nature of statistical learning theory
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Robust Real-Time Face Detection
International Journal of Computer Vision
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
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
Histogram feature-based Fisher linear discriminant for face detection
Neural Computing and Applications
Improving object detection with boosted histograms
Image and Vision Computing
Theoretical Analysis of a Performance Measure for Imbalanced Data
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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This paper addresses the problem of local histogram-based image feature selection for learning binary classifiers. We show a novel technique which efficiently combines histogram feature projection with the conditional mutual information (CMI) based classifier selection scheme. Moreover, we investigate cost-sensitive modifications of the CMI-based selection procedure, which further improves the classification performance. Extensive evaluations show that the proposed methods are suitable for object detection and recognition tasks.