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In the domains of dataset construction and crowdsourcing, a notable challenge is to aggregate labels from a heterogeneous set of labelers, each of whom is potentially an expert in some subset of tasks (and less reliable in others). To reduce costs of hiring human labelers or training automated labeling systems, it is of interest to minimize the number of labelers while ensuring the reliability of the resulting dataset. We model this as the problem of performing K-class classification using the predictions of smaller classifiers, each trained on a subset of [K], and derive bounds on the number of classifiers needed to accurately infer the true class of an unlabeled sample under both adversarial and stochastic assumptions. By exploiting a connection to the classical set cover problem, we produce a near-optimal scheme for designing such configurations of classifiers which recovers the well known one-vs.-one classification approach as a special case. Experiments with the MNIST and CIFAR-10 datasets demonstrate the favorable accuracy (compared to a centralized classifier) of our aggregation scheme applied to classifiers trained on subsets of the data. These results suggest a new way to automatically label data or adapt an existing set of local classifiers to larger-scale multiclass problems.
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User-Independent Detection of Swipe Pressure Using a Thermal Camera for Natural Surface Interaction
n this paper, we use a thermal camera to distinguish hard and soft swipes performed by a user interacting with a natural surface by detecting differences in the thermal signature of the surface due to heat transferred by the user. Unlike prior work, our approach provides swipe pressure classifiers that are user-agnostic, i.e., that recognize the swipe pressure of a novel user not present in the training set, enabling our work to be ported into natural user interfaces without user-specific calibration. Our approach generates average classification accuracy of 76% using random forest classifiers trained on a test set of 9 subjects interacting with paper and wood, with 8 hard and 8 soft test swipes per user. We compare results of the user-agnostic classification to user-aware classification with classifiers trained by including training samples from the user. We obtain average user-aware classification accuracy of 82% by adding up to 8 hard and 8 soft training swipes for each test user. Our approach enables seamless adaptation of generic pressure classification systems based on thermal data to the specific behavior of users interacting with natural user interfaces.
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- Award ID(s):
- 1730183
- PAR ID:
- 10094309
- Date Published:
- Journal Name:
- 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MMSP.2018.8547052
