Constructing Category Hierarchies for Visual Recognition

Abstract. Class hierarchies are commonly used to reduce the complexity of the classification problem. This is crucial when dealing with a large number of categories. In this work, we evaluate class hierarchies currently constructed for visual recognition. We show that top-down as well as bottom-up approaches, which are commonly used to automatically construct hierarchies, incorporate assumptions about the separability of classes. Those assumptions do not hold for visual recognition of a large number of object categories. We therefore propose a modification which is appropriate for most top-down approaches. It allows to construct class hierarchies that postpone decisions in the presence of uncertainty and thus provide higher recognition accuracy. We also compare our method to a one-against-all approach and show how to control the speed-foraccuracy trade-off with our method. For the experimental evaluation, we use the Caltech-256 visual object classes dataset and compare to stateof-the-a...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Marcin Marszalek, Cordelia Schmid
• Keywords: Class Hierarchies, Computer Vision, ECCV 2008, Large Number, Top-down Approaches, Visual Object Classes, Visual Recognition