Le prochain séminaire de l'équipe A3SI du LIGM (unité mixte de recherche de l'Université Paris Est) aura lieu le jeudi 15 septembre de 14h00 à 15h00 à ESIEE PARIS (salle 260).
Abstract: Many of the most successful vision algorithms are based on clever heuristics. We study two algorithms and show that, under certain statistical models, they correspond to common statistical decisions. The first is the local variation (LV) algorithm (LV) (Efficient graph-based image segmentation, by Felzenszwalb and Huttenlocher). We show that algorithms similar to LV can be devised by applying different statistical models and decisions, some of which are based on statistics of natural images and on a hypothesis testing decision. We we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, yields state-of-the-art results while preserving the computational complexity of the LV algorithm. We then turn to the SIFT matching algorithm (Object recognition from local scale-invariant features, by Lowe). Here we study the ratio criterion and show that this criterion could be a result of using various statistical decisions and, in particular, could correspond to minimizing the conditional probability of a false match. In both cases, the analysis provides further theoretical justification and well-founded explanations for the unexpected high performance of these two algorithms. It also provides statistically based versions of these algorithms that are at least as good as, and sometimes better than, the originals.
Joint work with Michael Baltaxe, Peter Meer, Avi Kaplan, and Tammy Avraham.