Section: Scientific Foundations

Pattern recognition and statistical learning

Participants : Nozha Boujemaa, Michel Crucianu, Donald Geman, Wajih Ouertani, Asma Rejeb Sfar.

Statistical learning and classification methods are of central interest for content-based image retrieval. 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 labeled 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.

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 generalization rules from training examples. The generalization 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.

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

• Solve the "page zero" problem by generating a visual summary of a database that takes into account all the available signatures together.

• Perform image segmentation by clustering local image descriptors.

• Structure and sort out the signature space for either global or local signatures, allowing a hierarchical search that is necessarily more efficient as it only requires to "scan" the representatives of the resulting clusters.

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.