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Feature Seeding for Action Recognition

Pyry K. Matikainen, Rahul Sukthankar and Martial Hebert
Conference Paper, Carnegie Mellon University, International Conference on Computer Vision 2011, November, 2011

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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 the features are seeded differs significantly from the real data, both in terms of appearance and the set of action classes.

BibTeX Reference
title = {Feature Seeding for Action Recognition},
author = {Pyry K. Matikainen and Rahul Sukthankar and Martial Hebert},
booktitle = {International Conference on Computer Vision 2011},
school = {Robotics Institute , Carnegie Mellon University},
month = {November},
year = {2011},
address = {Pittsburgh, PA},