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In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentifica ...
In this paper, we present a fast method to detect humans from videos captured in surveillance applications. It is based on a cascade of LogitBoost classifiers relying on features mapped from the Riemanian manifold of region covariance matrices computed fro ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...
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 ...
This paper describes a new approach to automatic frontal face detection which employs Gaussian filters as local image descriptors. We then show how the paradigm of classifier combination can be used for building a face detector that outperforms the current ...
Background: Speaker detection is an important component of many human-computer interaction applications, like for example, multimedia indexing, or ambient intelligent systems. This work addresses the problem of detecting the current speaker in audio-visual ...
Merging decisions from different modalities is a crucial problem in Audio-Visual Speech Recognition. To solve this, state synchronous multi-stream HMMs have been proposed for their important advantage of incorporating stream reliability in their fusion sch ...
Modeling long-term dependencies in time series has proved very difficult to achieve with traditional machine learning methods. This problem occurs when considering music data. In this paper, we introduce a model for rhythms based on the distributions of di ...
The modulation spectrum is an efficient representation for describing dynamic information in signals. In this work we investigate how to exploit different elements of the modulation spectrum for extraction of information in automatic recognition of speech ...
