Learning invariance through imitation

Supervised methods for learning an embedding aim to map high-dimensional images to a space in which perceptually similar observations have high measurable similarity. Most approaches rely on binary similarity, typically defined by class membership where labels are expensive to obtain and/or difficult to define. In this paper we propose crowdsourcing similar images by soliciting human imitations. We exploit temporal coherence in video to generate additional pairwise graded similarities between the user-contributed imitations. We introduce two methods for learning nonlinear, invariant mappings that exploit graded similarities. We learn a model that is highly effective at matching people in similar pose. It exhibits remarkable invariance to identity, clothing, background, lighting, shift and scale.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Graham Taylor, Ian Spiro, Rob Fergus, Christoph Bregler
• Keywords: Binary Similarity, Computer Vision, CVPR 2011, Pairwise Graded Similarities, Similar Observations