Learning object categories from Google’s image search
Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by utilizing the raw output of image search engines available on the Intern...
Autors principals: | Fergus, R, Fei-Fei, L, Perona, P, Zisserman, A |
---|---|
Format: | Conference item |
Idioma: | English |
Publicat: |
IEEE
2005
|
Ítems similars
-
Learning object categories from internet image searches
per: Fergus, R, et al.
Publicat: (2010) -
A visual category filter for Google Images
per: Fergus, R, et al.
Publicat: (2004) -
A sparse object category model for efficient learning and exhaustive recognition
per: Fergus, R, et al.
Publicat: (2005) -
A sparse object category model for efficient learning and complete recognition
per: Fergus, R, et al.
Publicat: (2006) -
Object class recognition by unsupervised scale-invariant learning
per: Fergus, R, et al.
Publicat: (2003)