Dans le cadre de cette étude, nous proposons d’analyser la langue militaire et particulièrementcelle de l’armée de Terre. Dans un premier temps, nous traitons de la terminologie, de lalangue militaire technique par rapport à la langue générale et d’un aperçu des dictionnaires despécialité et glossaires de la Défense. Dans une deuxième partie, nous nous appuyons sur lanotion des classes d’objets, instaurées par G. GROSS, qui demeure la meilleure méthoded’analyse de la langue militaire en étudiant les propriétés syntaxiques des prédicats nominauxd’action, les verbes supports et appropriés et leur actualisation. Pour illustrer et approfondirnotre travail, nous nous basons sur les actions des militaires en préparation d’une opération,pendant la projection et à l’issue de l’engagement. Par ces actions, nous démontrons toutes lespropriétés syntaxiques et sémantiques spécifiques à chaque classe de prédicats. La troisièmepartie, consacrée aux arguments de type humain, inanimé concret et locatif, est la suitelogique de l’étude des classes d’objets. Avec cette thèse, nous possédons tous les élémentspour envisager, par exemple une étude contrastive avec une autre langue et surtout grâce auxanalyses, la création d’un dictionnaire bilingue. / Within the framework of this study, we propose to analyse military language and particularlythat of the army. In part One, we deal with terminology, technical military language inrelation to everyday language as well as a survey of Defense-related specialized dictionariesand glossaries. In part Two, we take our cue from G. GROSS’s concept of object classes,which remains the best way of approaching military language specifically action-orientednominal predicates’syntactical properties, and support and appropriate verbs. We drew ourexamples from military action during fieldwork, both home and abroad, and upon the returnhome. Making use of these three phases, we outline syntactical and semantic propertiesspecific to each class of predicates. Part Three is devoted to arguments of three types : human,inanimate and locative ; it follows logically upon our analysis of object classes. Thisdissertation will help us carry out a contrastive analysis with another language as well as thecreation of a bilingual dictionary.

Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à l’approche un-contre-tous, notre méthode améliore les performances pour 20 classes et gagne 10x en vitesse. / Recent years have witnessed a competition in autonomous navigation for vehicles boosted by the advances in computer vision. The on-board cameras are capable of understanding the semantic content of the environment. A core component of this system is to localize and classify objects in urban scenes. There is a need to have multi-class object detection systems. Designing such an efficient system is a challenging and active research area. The algorithms can be found for applications in autonomous driving, object searches in images or video surveillance. The scale of object classes varies depending on the tasks. The datasets for object detection started with containing one class only e.g. the popular INRIA Person dataset. Nowadays, we witness an expansion of the datasets consisting of more training data or number of object classes. This thesis proposes a solution to efficiently learn a multi-class object detector. The task of such a system is to localize all instances of target object classes in an input image. We distinguish between three major efficiency criteria. First, the detection performance measures the accuracy of detection. Second, we strive low execution times during run-time. Third, we address the scalability of our novel detection framework. The two previous criteria should scale suitably with the number of input classes and the training algorithm has to take a reasonable amount of time when learning with these larger datasets. Although single-class object detection has seen a considerable improvement over the years, it still remains a challenge to create algorithms that work well with any number of classes. Most works on this subject extent these single-class detectors to work accordingly with multiple classes but remain hardly flexible to new object descriptors. Moreover, they do not consider all these three criteria at the same time. Others use a more traditional approach by iteratively executing a single-class detector for each target class which scales linearly in training time and run-time. To tackle the challenges, we present a novel framework where for an input patch during detection the closest class is ranked highest. Background labels are rejected as negative samples. The detection goal is to find the highest scoring class. To this end, we derive a convex problem formulation that combines ranking and classification constraints. The accuracy of the system is improved by hierarchically arranging the classes into a tree of classifiers. The leaf nodes represent the individual classes and the intermediate nodes called super-classes group recursively these classes together. The super-classes benefit from the shared knowledge of their descending classes. All these classifiers are learned in a joint optimization problem along with the previouslymentioned constraints. The increased number of classifiers are prohibitive to rapid execution times. The formulation of the detection goal naturally allows to use an adapted tree traversal algorithm to progressively search for the best class but reject early in the detection process the background samples and consequently reduce the system’s run-time. Our system balances between detection performance and speed-up. We further experimented with feature reduction to decrease the overhead of applying the high-level classifiers in the tree. The framework is transparent to the used object descriptor where we implemented the histogram of orientated gradients and deformable part model both introduced in [Felzenszwalb et al., 2010a]. The capabilities of our system are demonstrated on two challenging datasets containing different object categories not necessarily semantically related. We evaluate both the detection performance with different number of classes and the scalability with respect to run-time. Our experiments show that this framework fulfills the requirements of a multi-class object detector and highlights the advantages of structuring class-level knowledge.