Semantic label sharing for learning with many categories
Name
Torralba_Semantic label.pdf
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2.32 MB
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Adobe PDF
Checksum (MD5)
7a49d09c905443e743ce41ce642ee90a
Author(s)
Fergus, Rob
Bernal, Hector
Weiss, Yair
Torralba, Antonio
Date Issued
September 2010
Journal
Proceedings of the 11th European conference on Computer vision: Part I, ECCV'10
Publisher
Association for Computing Machinery
Citation
Fergus, Rob et al. “Semantic Label Sharing for Learning with Many Categories.” in Proceedings of the 11th European Conference on Computer Vision: Part I. Heraklion, Crete, Sept. 5-11, Greece: Springer-Verlag, 2010. 762-775. (Lecture Notes in Computer Science, Vol. 6311)
Version
Author's final manuscript
Abstract
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, up to 80 million images and 75,000 categories in size, show that despite the simplicity of the approach, it leads to significant improvements in performance.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://portal.acm.org/citation.cfm?id=1886121
