Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Fragment-Based Approach to Object Representation and Classification
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Efficient Learning of Relational Object Class Models
International Journal of Computer Vision
A Performance Evaluation of Single and Multi-feature People Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Unsupervised Classification and Part Localization by Consistency Amplification
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Improved feature selection algorithm based on SVM and correlation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Monocular rear-view obstacle detection using residual flow
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, termed Predictive Feature Selection (PFS), which is governed by weight prediction. The algorithm makes it possible to choose from O(106) features in an efficient but accurate manner. We analyze the validity and behavior of PFS and empirically demonstrate its speed and accuracy advantages over relevant competitors. We present an empirical evaluation of our method on three human detection datasets including the current de-facto benchmarks (the INRIA and Caltech pedestrian datasets) and a new challenging dataset of children images in difficult poses. The evaluation suggests that our approach is on a par with the best current methods and advances the state-of-the-art on the Caltech pedestrian training dataset.