A sparse object category model for efficient learning and exhaustive recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse repr...
Autors principals: | Fergus, R, Perona, P, Zisserman, A |
---|---|
Format: | Conference item |
Idioma: | English |
Publicat: |
IEEE
2005
|
Ítems similars
-
A sparse object category model for efficient learning and complete recognition
per: Fergus, R, et al.
Publicat: (2006) -
Learning object categories from Google’s image search
per: Fergus, R, et al.
Publicat: (2005) -
Learning object categories from internet image searches
per: Fergus, R, et al.
Publicat: (2010) -
Object class recognition by unsupervised scale-invariant learning
per: Fergus, R, et al.
Publicat: (2003) -
A visual category filter for Google Images
per: Fergus, R, et al.
Publicat: (2004)