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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Semantic Label Sharing for Learning with Many Categories
verfasst von : Rob Fergus, Hector Bernal, Yair Weiss, Antonio Torralba
Erschienen in: Computer Vision – ECCV 2010
Verlag: Springer Berlin Heidelberg
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In an object recognition scenario with tens of thousands of categories, even a small number of labels per category leads to a very large number of total labels required. We propose a simple method of
label sharing
between semantically similar categories. We leverage the WordNet hierarchy to define semantic distance between any two categories and use this semantic distance to share labels. Our approach can be used with any classifier. Experimental results on a range of datasets, upto 80 million images and 75,000 categories in size, show that despite the simplicity of the approach, it leads to significant improvements in performance.