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Keypoint Signatures for Fast Learning and Recognition

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Ensembles of SVMs using an Information Theoretic Criterion

Jean-Philippe Thiran, Julien Meynet

Training Support Vector Machine can become very challenging in large scale problems. Training several lower complexity SVMs on local subsets of the training set can significantly reduce the training complexity and also improve the classification performanc ...

2008

Information theoretic combination of classifiers with application to face detection

Julien Meynet

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...

EPFL2007

A multimodal pattern recognition framework for speaker detection

Patricia Besson

Speaker detection is an important component of a speech-based user interface. Audiovisual speaker detection, speech and speaker recognition or speech synthesis for example find multiple applications in human-computer interaction, multimedia content indexin ...

EPFL2007

Fast Multiview Face Tracking with Pose Estimation

Jean-Philippe Thiran, Javier Cruz Mota, Julien Meynet

In this paper, a fast and effective multi-view face tracking algorithm with head pose estimation is introduced. For modeling the face pose we employ a tree of boosted classifiers built using either Haar-like filters or Gauss filters. A first classifier ext ...

2007

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is ...

2005

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...

2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

2004

Online Policy Adaptation for Ensemble Classifiers

Samy Bengio, Christos Dimitrakakis

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...

2004

Towards Recognizing Feature Points using Classification Trees

Pascal Fua, Vincent Lepetit

In earlier work~\cite{Lepetit04b}, we proposed to treat wide baseline matching of feature points as a classification problem and proposed an implementation based on K-means and nearest neighbor classification. We showed that this method is both reliable an ...

2004

Randomized Trees for Real-Time Keypoint Recognition

Pascal Fua, Vincent Lepetit, Pascal Lagger

In earlier work, we proposed treating wide baseline matching of feature points as a classification problem, in which each class corresponds to the set of all possible views of such a point. We used a K-mean plus Nearest Neighbor classifier to validate our ...

2004