In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource data (satellite multispectral images, elevation, slope and aspect data) for landcover classification. In a second part, special attention is gi...

In the recent years, pixel-wise classification of hyperspectral images aroused many developments, and the literature now provides various classifiers for numerous applications. In this chapter, we present a generic framework where the redundant or complementary results provided by multiple classifiers can actually be aggregated. Taking advantage from the specificities of each classifier, the de...

Classifying species by their sounds is a fundamental challenge in the study of animal vocalisations. Most of existing studies are based on manual inspection and labelling of acoustic features, e.g. amplitude signals and sound spectra, which relies on the agreement between human experts. But during the last ten years, systems for the automated classification of animal vocalisations have been dev...

Because higherand lower-level policies do not necessarily correspond one-to-one, a higher-level network policy may have to be translated into two or more lowerlevel policies, and two or more cooperating higher-level policies may have to be translated into one lower-level policy. The former transformation is called a policy division, and the latter transformation is called a policy fusion. These...

Recently many researchers concentrate on Multiple Classifier System (MCS) in pattern recognition. Pattern recognition system build in three steps i.e. database, feature extraction and classifier. MCS is Architect by combining more than one classifier i.e. either same or different classifiers for different pattern recognition applications such as emotion recognition, character recognition, face ...

Although the large number of MCS topics, serial fusion of multiple classifiers has been poorly investigated so far. In this paper, we propose a model which, starting from the performance of individual classifiers and the traditional hypothesis of decision independence given the class, is able to estimate the performance, in terms of error rates, of the whole serial classification scheme. The mo...

We address the problem of complicated event categorization from a large dataset of videos “in the wild”, where multiple classifiers are applied independently to evaluate each video with a ‘likelihood’ score. The core contribution of this paper is a local expert forest model for meta-level score fusion for event detection under heavily imbalanced class distributions. Our motivation is to adapt t...

In this paper, we summarize how the action recognition can be improved when multiple views are available. The novelty is that we explore various combination schemes within the robust and simple bag-of-words (BoW) framework, from early fusion of features to late fusion of multiple classifiers. In new experiments on the publicly available IXMAS dataset, we learn that action recognition can be imp...

This paper proposes a new ensemble method that constructs an ensemble of tree-structured classifiers using multi-view learning. We are motivated by the fact that an ensemble can outperform its members providing that these classifiers are diverse and accurate. In order to construct diverse individual classifiers, we assume that the object to be classified is described by multiple feature sets (v...