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ECCV
2006
Springer

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

9 years 11 months ago
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...
David J. Crandall, Daniel P. Huttenlocher
Added 16 Oct 2009
Updated 16 Oct 2009
Type Conference
Year 2006
Where ECCV
Authors David J. Crandall, Daniel P. Huttenlocher
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