Mémoire en recherche-création / Ce mémoire a pour objectif d’explorer les différents moyens cinématographiques mis en place par le cinéaste américain David Lynch afin de comprendre comment ce dernier parvient à transporter son audience dans un monde fantastique et bien sûr, étrange. En nous appuyant sur les premières définitions freudiennes de ce que représente l’inquiétante étrangeté (Das Unheimliche), nous explorerons les codes et images récurrentes dans la cinématographie lynchéenne qui font des films du réalisateur des œuvres aux caractères étranges, après avoir étudié en détail les éléments qui nous permettent de classer les œuvres de l’auteur dans la catégorie des œuvres fantastiques. / This thesis aims to explore the different cinematographic means implemented by David Lynch in order to understand how he manages to transport his audience in a fantastic and of course, strange world. By informing ourselves about the first Freudian definitions of what represents the uncanny (Das Unheimliche), we will explore the recurrent codes and images in Lynchian cinematography which make the director’s films works uncanny, right after having studied in detail the elements that allow us to classify the author’s works in the fantastic genre section.

Recognition of complex events in consumer uploaded Internet videos, captured under realworld settings, has emerged as a challenging area of research across both computer vision and multimedia community. In this dissertation, we present a systematic decomposition of complex events into hierarchical components and make an in-depth analysis of how existing research are being used to cater to various levels of this hierarchy and identify three key stages where we make novel contributions, keeping complex events in focus. These are listed as follows: (a) Extraction of novel semi-global features – firstly, we introduce a Lie-algebra based representation of dominant camera motion present while capturing videos and show how this can be used as a complementary feature for video analysis. Secondly, we propose compact clip level descriptors of a video based on covariance of appearance and motion features which we further use in a sparse coding framework to recognize realistic actions and gestures. (b) Construction of intermediate representations – We propose an efficient probabilistic representation from low-level features computed from videos, based on Maximum Likelihood Estimates which demonstrates state of the art performance in large scale visual concept detection, and finally, (c) Modeling temporal interactions between intermediate concepts – Using block Hankel matrices and harmonic analysis of slowly evolving Linear Dynamical Systems, we propose two new discriminative feature spaces for complex event recognition and demonstrate significantly improved recognition rates over previously proposed approaches.