ARTEMIS: Automatic Recognition of Training Examples for Modeling Image
Tagged Web images provide an abundance of labeled
training examples for visual concept learning. However, the
performance of automatic training data selection is susceptible
to highly inaccurate tags and atypical images. Consequently,
manually curated training datasets are still a preferred choice
for many image annotation systems. This work introduces
`ARTEMIS' - a scheme to enhance automatic selection of
training images using an instance-weighted mixture modeling
framework. An optimization algorithm is derived that in
addition to mixture parameter estimation learns instance-weights,
essentially adapting to the noise associated with each example.
The mechanism of hypothetical local mapping is evoked so
that data in diverse mathematical forms or modalities can
be cohesively treated as the system maintains tractability in
optimization. Finally, training examples are selected from topranked
images of a likelihood-based image ranking. Experiments
indicate that ARTEMIS exhibits higher resilience to noise
than several baselines for large training data collection. The
performance of ARTEMIS-trained image annotation system is
comparable to using manually curated datasets.
- Neela Sawant, James Z. Wang and Jia Li, ``Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach,'' IEEE Transactions on Image Processing, accepted, April 2013.
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This material is based
upon work supported by the National Science
Foundation. Any opinions, findings, and conclusions or
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© 2013- Neela Sawant, James Z. Wang, Jia Li
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