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Feature Seeding for Action Recognition



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Feature Seeding for Action Recognition Pyry Matikainen Rahul Sukthankar Martial Hebert pmatikai cs cmu edu rahuls cs cmu edu hebert ri cmu edu The Robotics Institute Carnegie Mellon University 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 the features are seeded differs significantly from the real data both in terms of appearance and the set of action classes 1 Introduction A human researcher who designs a feature has an almost insurmountable advantage over a learning algorithm they can appeal to an intuition built over thousands of hours of direct experience with the world to decide which parts of the visual experience are important to consider and which are noise In contrast an algorithm that attempts to select or learn features directly from a target dataset risks overfitting especially if a large number of candidate features are considered Intuitively this problem might be avoided if a large amount of related data were used to learn the features one promising method to produce such data is synthetic generation using computer graphics techniques While graphics methods are not quite yet at the point where plausible synthetic images can be economically generated in the special case of motion the widespread availability of mature motion capture technology has provided a wealth of resources from which synthetic videos of human motion can be produced We



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