Team Imedia

Overall Objectives
Scientific Foundations
Application Domains
New Results
Other Grants and Activities

Section: Scientific Foundations

Pattern recognition and statistical learning

Statistical learning and classification methods are of central interest for content-based image retrieval [35] [41] .

We consider here both supervised and unsupervised methods. Depending on our knowledge of the contents of a database, we may or may not be provided with a set of labelled training examples . For the detection of known objects, methods based on hierarchies of classifiers have been investigated. In this context, face detection was a main topic, as it can automatically provide a high-level semantic information about video streams. For a collection of pictures whose content is unknown, e.g. in a navigation scenario, we are investigating techniques that adaptively identify homogeneous clusters of images, which represent a challenging problem due to feature space configuration.

Statistical learning and object detection

Keywords : Statistical learning, boosting, object detection, object retrieval, kernel methods.

Participants : Donald Geman, Nozha Boujemaa, Nicolas Hervé, Alexis Joly, Ahmed Rebai.

Object detection is the most straightforward solution to the challenge of content-based image indexing. Classical approaches (artificial neural networks, support vector machines, etc.) are based on induction, they construct generalisation rules from training examples. The generalisation error of these techniques can be controlled, given the complexity of the models considered and the size of the training set.

Our research on object detection addresses the design of invariant kernels and algorithmically efficient solutions as well as boosting method for similarity learning. We have developed several algorithms for face detection based on a hierarchical combination of simple two-class classifiers. Such architectures concentrate the computation on ambiguous parts of the scene and achieve error rates as good as those of far more expensive techniques.

Clustering methods

Keywords : clustering, membership, number of classes, pattern recognition, competitive agglomeration.

Participants : Nozha Boujemaa, Michel Crucianu, Hervé Goëau, Itheri Yahiaoui, Nicolas Hervé.

Unsupervised clustering techniques automatically define categories and are for us a matter of visual knowledge discovery. We need them in order to:

Given the complexity of the feature spaces we are considering, this is a very difficult task. Noise and class overlap challenge the estimation of the parameters for each cluster. The main aspects that define the clustering process and inevitably influence the quality of the result are the clustering criterion, the similarity measure and the data model.

We investigate a family of clustering methods based on the competitive agglomeration that allows us to cope with our primary requirements: estimate the unknown number of classes, handle noisy data and deal with classes (by using fuzzy memberships that delay the decision as much as possible).


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