Sylvain Lesage - Academia.edu (original) (raw)

Papers by Sylvain Lesage

Research paper thumbnail of Learning multimodal dictionaries

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2007

Real-world phenomena involve complex interactions between multiple signal modalities. As a conseq... more Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions...

Research paper thumbnail of Learning Multimodal Dictionaries

IEEE Transactions on Image Processing, 2000

Real-world phenomena involve complex interactions between multiple signal modalities. As a conseq... more Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multi-modal data can in fact reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multi-modal patterns could offer a deep insight into the structure of such signals. Typically, such recurrent patterns are shift invariant, thus the learning should try to find the best matching filters. In this paper we present an algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and we show that it is able to discover underlying structures in the data.

Research paper thumbnail of Learning redundant dictionaries with translation invariance property: the MoTIF algorithm

Sparse approximation using redundant dictionaries is an efficient tool for many applications in t... more Sparse approximation using redundant dictionaries is an efficient tool for many applications in the field of signal processing. The performances largely depend on the adaptation of the dictionary to the signal to decompose. As the statistical dependencies are most of the time not obvious in natural highdimensional data, learning fundamental patterns is an alternative to analytical design of bases and has become a field of acute research. Most of the time, the underlying patterns of a class of signals can be found at any time, and in the design of a dictionary, this translation invariance property should be present. We present a new algorithm for learning short generating functions, each of them building a set of atoms corresponding to all its translations. The resulting dictionary is highly redundant and translation invariant.

Research paper thumbnail of Learning multi-modal dictionaries: Application to audiovisual data

This paper presents a methodology for extracting meaningful synchronous structures from multi-mod... more This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural high-dimensional data, the statistical dependencies between modalities are, most of the time, not obvious. Learning fundamental multi-modal patterns is an alternative to classical statistical methods. Typically, recurrent patterns are shift invariant, thus the learning should try to find the best matching filters. We present a new algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal. The proposed algorithm is applied to audiovisual sequences and it demonstrates to be able to discover underlying structures in the data.

Research paper thumbnail of Analyse des patrimoines de données géographiques nationaux. Comparaison de trois infrastructures nationales dedonnées géographiques (France, Brésil, Bolivie)

Par le double effet de l'interopérabilité des systèmes et de l'évolution du cadre légal, les patr... more Par le double effet de l'interopérabilité des systèmes et de l'évolution du cadre légal, les patrimoines de données géographiques institutionnelles tendent aujourd'hui à être de plus en plus facilement accessibles à travers la mise en place généralisée d'infrastructures de données géographiques (IDG). L'analyse comparée du contenu de trois IDG nationales (France, Bolivie, Brésil) permet alors de révéler quelques tendances. L'originalité de cette contribution est donc de faire des IDG non pas un support à la recherche d'information mais un véritable objet de recherche. L'analyse des métadonnées des trois catalogues nationaux nous permettra alors de réinterroger les logiques de formalisation et de diffusion des connaissances sur les territoires.

Research paper thumbnail of Learning multimodal dictionaries

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2007

Real-world phenomena involve complex interactions between multiple signal modalities. As a conseq... more Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions...

Research paper thumbnail of Learning Multimodal Dictionaries

IEEE Transactions on Image Processing, 2000

Real-world phenomena involve complex interactions between multiple signal modalities. As a conseq... more Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multi-modal data can in fact reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multi-modal patterns could offer a deep insight into the structure of such signals. Typically, such recurrent patterns are shift invariant, thus the learning should try to find the best matching filters. In this paper we present an algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and we show that it is able to discover underlying structures in the data.

Research paper thumbnail of Learning redundant dictionaries with translation invariance property: the MoTIF algorithm

Sparse approximation using redundant dictionaries is an efficient tool for many applications in t... more Sparse approximation using redundant dictionaries is an efficient tool for many applications in the field of signal processing. The performances largely depend on the adaptation of the dictionary to the signal to decompose. As the statistical dependencies are most of the time not obvious in natural highdimensional data, learning fundamental patterns is an alternative to analytical design of bases and has become a field of acute research. Most of the time, the underlying patterns of a class of signals can be found at any time, and in the design of a dictionary, this translation invariance property should be present. We present a new algorithm for learning short generating functions, each of them building a set of atoms corresponding to all its translations. The resulting dictionary is highly redundant and translation invariant.

Research paper thumbnail of Learning multi-modal dictionaries: Application to audiovisual data

This paper presents a methodology for extracting meaningful synchronous structures from multi-mod... more This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural high-dimensional data, the statistical dependencies between modalities are, most of the time, not obvious. Learning fundamental multi-modal patterns is an alternative to classical statistical methods. Typically, recurrent patterns are shift invariant, thus the learning should try to find the best matching filters. We present a new algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal. The proposed algorithm is applied to audiovisual sequences and it demonstrates to be able to discover underlying structures in the data.

Research paper thumbnail of Analyse des patrimoines de données géographiques nationaux. Comparaison de trois infrastructures nationales dedonnées géographiques (France, Brésil, Bolivie)

Par le double effet de l'interopérabilité des systèmes et de l'évolution du cadre légal, les patr... more Par le double effet de l'interopérabilité des systèmes et de l'évolution du cadre légal, les patrimoines de données géographiques institutionnelles tendent aujourd'hui à être de plus en plus facilement accessibles à travers la mise en place généralisée d'infrastructures de données géographiques (IDG). L'analyse comparée du contenu de trois IDG nationales (France, Bolivie, Brésil) permet alors de révéler quelques tendances. L'originalité de cette contribution est donc de faire des IDG non pas un support à la recherche d'information mais un véritable objet de recherche. L'analyse des métadonnées des trois catalogues nationaux nous permettra alors de réinterroger les logiques de formalisation et de diffusion des connaissances sur les territoires.