Pose-Sensitive Embedding by Nonlinear NCA Regression

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Bibtex »Metadata »Paper »Supplemental »


Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro, Christoph Bregler


<p>This paper tackles the complex problem of visually matching people in similar pose but with different clothes, background, and other appearance changes. We achieve this with a novel method for learning a nonlinear embedding based on several extensions to the Neighborhood Component Analysis (NCA) framework. Our method is convolutional, enabling it to scale to realistically-sized images. By cheaply labeling the head and hands in large video databases through Amazon Mechanical Turk (a crowd-sourcing service), we can use the task of localizing the head and hands as a proxy for determining body pose. We apply our method to challenging real-world data and show that it can generalize beyond hand localization to infer a more general notion of body pose. We evaluate our method quantitatively against other embedding methods. We also demonstrate that real-world performance can be improved through the use of synthetic data.</p>