Semaine 7 jeudi

~ jeudi ~

Attribute base classification - a two layer classification that lets you relate the image information to a bunch of high level attributes that human use to describe objects, then save these attribute information for training, and discard the low-level image features. The attribute is a layer between the final classification result and the low-level image feature.

Once a classifier is trained, it can also learn new objects that were not in the training, as long as it can be described by the attributes. The method is used to illustrate that a more realistic testing is possible with this method, that it can identify objects just by imagining the high level attributes, like humans naturally do, and do not have to have seen an example of such a category beforehand.

Paper
http://www.kyb.mpg.de/publications/attachments/CVPR2009-Lampert_%5B0%5D.pdf [7]

Dataset
http://attributes.kyb.tuebingen.mpg.de/

Reference:
[7] C. H. Lampert, H. Nickisch, and S. Harmeling. Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer. In CVPR, 2009. http://www.kyb.mpg.de/publications/attachments/CVPR2009-Lampert_%5B0%5D.pdf