Feature Seeding for Action Recognition
Venue
International Conference on Computer Vision (ICCV) (2011)
Publication Year
2011
Authors
Pyry Matikainen, Rahul Sukthankar, Martial Hebert
BibTeX
Abstract
Progress in action recognition has been in large part due to advances in the
features that drive learning-based methods. However, the relative sparsity of
training data and the risk of overfitting have made it difficult to directly search
for good features. In this paper, we suggest using synthetic data to search for
robust features that can more easily take advantage of limited data, rather than
using the synthetic data directly as a substitute for real data. We demonstrate
that the features discovered by our selection method, which we call seeding,
improve performance on an action classification task on real data, even though the
synthetic data from which our features are seeded differs significantly from the
real data, both in terms of appearance and the set of action classes.
