Quelltext der Seite Relative attributes




== Referenz == 
D. Parikh and K. Grauman: Relative attributes. In International Conference on Computer Vision (ICCV), 2011. 

== DOI ==
http://dx.doi.org/10.1109/ICCV.2011.6126281

== Abstract ==
Human-nameable visual “attributes” can benefit various recognition tasks. However, existing techniques restrict these properties to categorical labels (for example, a person is `smiling' or not, a scene is `dry' or not), and thus fail to capture more general semantic relationships. We propose to model relative attributes. Given training data stating how object/scene categories relate according to different attributes, we learn a ranking function per attribute. The learned ranking functions predict the relative strength of each property in novel images. We then build a generative model over the joint space of attribute ranking outputs, and propose a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, `bears are furrier than giraffes'). We further show how the proposed relative attributes enable richer textual descriptions for new images, which in practice are more precise for human interpretation. We demonstrate the approach on datasets of faces and natural scenes, and show its clear advantages over traditional binary attribute prediction for these new tasks.

== Extended Abstract ==

== Bibtex == 
 @INPROCEEDINGS{6126281,
 author={D. Parikh and K. Grauman},
 booktitle={2011 International Conference on Computer Vision},
 title={Relative attributes},
 year={2011},
 pages={503-510},
 keywords={face recognition;learning (artificial intelligence);natural scenes;binary attribute prediction;categorical label;face dataset;human interpretation;human-name visual attribute;image textual description;learned ranking function;natural scene;object category;ranking function per attribute;recognition task;scene category;training data;zero-shot learning;Humans;Image recognition;Machine learning;Support vector machines;Training;Visualization;Vocabulary},
 doi={10.1109/ICCV.2011.6126281},
 url={http://dx.doi.org/10.1109/ICCV.2011.6126281 http://www.cs.utexas.edu/~grauman/papers/ParikhGrauman_ICCV2011_relative.pdf http://de.evo-art.org/index.php?title=Relative_attributes },
 ISSN={1550-5499},
 month={Nov},
 }

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== Links == 

=== Full Text === 
http://www.cs.utexas.edu/~grauman/papers/ParikhGrauman_ICCV2011_relative.pdf

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=== Sonstige Links ===