Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition

Abstract. In this paper we investigate a new method of learning partbased models for visual object recognition, from training data that only provides information about class membership (and not object location or configuration). This method learns both a model of local part appearance and a model of the spatial relations between those parts. In contrast, other work using such a weakly supervised learning paradigm has not considered the problem of simultaneously learning appearance and spatial models. Some of these methods use a "bag" model where only part appearance is considered whereas other methods learn spatial models but only given the output of a particular feature detector. Previous techniques for learning both part appearance and spatial relations have instead used a highly supervised learning process that provides substantial information about object part location. We show that our weakly supervised technique produces better results than these previous highly supervi...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: David J. Crandall, Daniel P. Huttenlocher
• Keywords: Computer Vision, ECCV 2006, Richer Appearance Models, Richer Spatial Models, Supervised Learning Process, Weakly Supervised Learning, Weakly Supervised Technique