Sparse Flexible Models of Local Features

Abstract. In recent years there has been growing interest in recognition models using local image features for applications ranging from long range motion matching to object class recognition systems. Currently, many state-of-the-art approaches have models involving very restrictive priors in terms of the number of local features and their spatial relations. The adoption of such priors in those models are necessary for simplifying both the learning and inference tasks. Also, most of the state-of-the-art learning approaches are semi-supervised batch processes, which considerably reduce their suitability in dynamic environments, where unannotated new images are continuously presented to the learning system. In this work we propose: 1) a new model representation that has a less restrictive prior on the geometry and number of local features, where the geometry of each local feature is influenced by its k closest neighbors and models may contain hundreds of features; and 2) a novel unsuperv...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Gustavo Carneiro, David Lowe
• Keywords: Class Recognition Systems, Computer Vision, ECCV 2006, On-line Learning Algorithm, Restrictive Priors, State-of-the-art Learning Approaches, Visual Class Recognition