(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision
Computational visual perception seeks to reproduce human visionthrough the combination of visual sensors, artificial intelligence andcomputing. To this end, computer vision tasks are often reformulatedas mathematical inference problems where the objective is to determinethe set of parameters corresponding to the lowest potential of a taskspecificobjective function. Graphical models have been the most popularformulation in the field over the past two decades where the problemis viewed as a discrete assignment labeling one. Modularity, scalabilityand portability are the main strengths of these methods which oncecombined with efficient inference algorithms they could lead to state ofthe art results. In this tutorial we focus on the inference component ofthe problem and in particular we discuss in a systematic manner themost commonly used optimization principles in the context of graphicalmodels. Our study concerns inference over low rank models interactionsbetween variables are constrained to pairs as well as higher orderones arbitrary set of variables determine hyper-cliques on which constraintsare introduced and seeks a concise, self-contained presentationof prior art as well as the presentation of the current state of the artmethods in the field.