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Published Articles >> Table of Contents >> Abstract
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2
pp. 623-630
Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification
Matthew Boutell, University of Rochester
Jiebo Luo, Eastman Kodak Company
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.49
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| Abstract |
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Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provides cues independent of the captured scene content that can be used to improve classification performance. We consider two problems: indoor-outdoor classification and sunset detection. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for both indoor-outdoor and sunset classification. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully, even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the two problems, using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.
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 Additional Information
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Citation:
Matthew Boutell, Jiebo Luo,
"Bayesian Fusion of Camera Metadata Cues in Semantic Scene Classification,"
cvpr,
pp. 623-630,
2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04) - Volume 2,
2004
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