Information compression in the context model

The Context Model provides a formal framework for the representation, interpretation, and analysis of vague and uncertain data. The clear semantics of the underlying concepts make it feasible to compare well-known approaches to the modeling of imperfect knowledge like that given in Bayes Theory, Shafer's Evidence Theory, the Transferable Belief Model, and Possibility Theory. In this paper we present the basic ideas of the Context Model and show its applicability as an alternative foundation of Possibility Theory and the epistemic view of fuzzy sets

Lymphocite segmentation using mixture of Gaussians and the transferable belief model.

International audienceIn the context of several pathologies, the presence of lym- phocytes has been correlated with disease outcome. The ability to au- tomatically detect lymphocyte nuclei on histopathology imagery could potentially result in the development of an image based prognostic tool. In this paper we present a method based on the estimation of a mixture of Gaussians for determining the probability distribution of the princi- pal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, the Transfer- able Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccen- tricity constraint principle

A hierarchical fusion of expert opinion in the TBM

This is an abridged version of http://halshs.archives-ouvertes.fr/halshs-00112129/fr/We define a hierarchical method for expert opinion aggregation that combines consonant beliefs in the Transferable Belief Model. Experts are grouped into schools of thought, then opinions are aggregated using the cautious conjunction operator within groups and the non-interactive disjunction across. This method is illustrated with a real-world dataset including 16 experts

Multispectral object segmentation and retrieval in surveillance video

This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The transferable belief model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenari

Hierarchical fusion of expert opinion in the Transferable Belief Model, application on climate sensitivity

International audienceThis paper examines the fusion of conflicting and not independent expert opinion in the Transferable Belief Model. Regarding procedures that combine opinions symmetrically, when beliefs are bayesian the non-interactive disjunction works better than the non-interactive conjunction, cautious conjunction or Dempster's combination rule.Then a hierarchical fusion procedure based on the partition of experts into schools of thought is introduced, justified by the sociology of science concepts of epistemic communities and competing theories. Within groups, consonant beliefs are aggregated using the cautious conjunction operator, to pool together distinct streams of evidence without assuming that experts are independent. Across groups, the non-interactive disjunction is used, assuming that when several scientific theories compete, they can not be all true at the same time, but at least one will remain. This procedure balances points of view better than averaging: the number of experts holding a view is not essential.This is illustrated with a 16 experts real-world dataset on climate sensitivity from 1995. Climate sensitivity is a key parameter to assess the severity of the global warming issue. Comparing our findings with recent results suggests that, unfortunately, the plausibility that sensitivity is small (below 1.5C) has decreased since 1995, while the plausibility that it is above 4.5C remains high.Ce texte examine la fusion des opinions d'experts en situation de controverse scientifique, à l'aide du Modèle des Croyances Transférables.Parmi les procédures qui combinent les experts symétriquement, nous constatons que lorsque les croyances sont bayésiennes (une modélisation classique s'appuyant sur les probabilités), l'opérateur de disjonction non-interactif donne de meilleurs résultats que les autres (conjonction prudente, la conjonction non-interactive, règle de Dempster).Puis nous proposons une procédure de fusion hiérarchique. En premier lieu, une partition des experts en écoles de pensée est réalisée à l'aide des méthodes de sociologie des sciences. Puis les croyances sont agrégées à l'intérieur des groupes avec l'opérateur de conjonction prudente: on suppose que tous les experts sont fiables, mais pas qu'ils constituent des sources d'information indépendantes entre elles. Enfin les groupes sont combinés entre eux par l'opérateur de disjonction non-interactive: on suppose qu'au moins l'une des écoles de pensée s'imposera, sans dire laquelle. Cette procédure offre un meilleur équilibre des points de vue que la simple moyenne, en particulier elle ne pondère pas les opinions par le nombre d'experts qui y souscrivent.La méthode est illustrée avec un jeu de données de 1995 obtenu en interrogeant 16 experts à propos de la sensibilité climatique (le paramètre clé exprimant la gravité du problème du réchauffement global). La comparaison de nos résultats avec la littérature récente montre que, hélas, la plausibilité que ce paramètre soit relativement faible (moins que 1.5C) a diminué depuis 1995, alors que la plausibilité qu'il soit au delà de 4.5C n'a pas décru