Visual Object Class Recognition combining Generative and Discriminative Methods

Abstract

Summary form only given. We describe various approaches capable of simultaneous recognition and localization of multiple object classes using a combination of generative and discriminative methods. A first approach uses a novel hierarchical representation allows to represent individual images as well as various objects classes in a single similarity invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. A second approach uses a dense representation and a topic distribution model to obtain an intermediate and general representation that is shared across object categories. Combined with discriminative methods these systems show excellent performance on several object categories.