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Semantic coding by supervised dimensionality reduction

Publications associées (91)

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Redundant Image Representations in Security Applications

Pascal Frossard, Pierre Vandergheynst, Philippe Jost

To be efficient, data protection algorithms should generally exploit the properties of the media information in the transform domain. In this paper, we will advocate the use of non-linear image approximations using highly redundant dictionaries, for securi ...

IEEE2004

Adaptive Kernel Matching Pursuit for Pattern Classification

Jean-Philippe Thiran, Vlad Popovici

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the numb ...

IEEE2004

New support vector algorithms for multicategorical data applied to real-time object recognition

Silvio Borer

Humans have the ability to learn. Having seen an object we can recognise it later. We can do this because our nervous system uses an efficient and robust visual processing and capabilities to learn from sensory input. On the other hand, designing algorithm ...

EPFL2003

Face Detection using SVM Trained in Eigenfaces Space

Jean-Philippe Thiran, Vlad Popovici

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of ei ...

SPIE2003

Evaluation of Formant-Like Features for ASR

Hervé Bourlard, Samy Bengio, Katrin Weber

This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been ...

IDIAP2002

Evaluation of Formant-Like Features for ASR

Hervé Bourlard, Samy Bengio, Katrin Weber

2002

Rotation and scale invariant shape representation and recognition using Matching Pursuit

Jean-Philippe Thiran, Pierre Vandergheynst, François Mendels

Using a low-level representation of images, like matching pursuit, we introduce a new way of describing objects through a general description using a translation, rotation, and isotropic scale invariant dictionary of basis functions. We then use this descr ...

IEEE2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000