Journal
INTERNATIONAL JOURNAL OF COMPUTER VISION
Volume 88, Issue 2, Pages 169-188Publisher
SPRINGER
DOI: 10.1007/s11263-009-0268-3
Keywords
Object recognition; Gaussian process; Kernel combination; Active learning
Categories
Funding
- NSF [0747356]
- Microsoft Research New Faculty
- Texas Higher Education Coordinating Board [003658-01-40-2007]
- DARPA VIRAT
- Henry Luce Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0747356] Funding Source: National Science Foundation
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Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially when the amount of training data labels that may be sought for a category is ultimately very small.
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