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Multi-attribute spaces: Calibration for attribute fusion and similarity search

6 years 9 months ago
Multi-attribute spaces: Calibration for attribute fusion and similarity search
Recent work has shown that visual attributes are a powerful approach for applications such as recognition, image description and retrieval. However, fusing multiple attribute scores – as required during multi-attribute queries or similarity searches – presents a significant challenge. Scores from different attribute classifiers cannot be combined in a simple way; the same score for different attributes can mean different things. In this work, we show how to construct normalized “multi-attribute spaces” from raw classifier outputs, using techniques based on the statistical Extreme Value Theory. Our method calibrates each raw score to a probability that the given attribute is present in the image. We describe how these probabilities can be fused in a simple way to perform more accurate multiattribute searches, as well as enable attribute-based similarity searches. A significant advantage of our approach is that the normalization is done after-the-fact, requiring neither modi...
Walter J. Scheirer, Neeraj Kumar, Peter N. Belhume
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Walter J. Scheirer, Neeraj Kumar, Peter N. Belhumeur, Terrance E. Boult
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