TY - JOUR
T1 - 80 million tiny images
T2 - A large data set for nonparametric object and scene recognition
AU - Torralba, Antonio
AU - Fergus, Rob
AU - Freeman, William T.
N1 - Funding Information:
Funding for this research was provided by NGA NEGI-1582-04-0004, Shell Research, Google, US Office of Naval Research MURI Grant N00014-06-1-0734, and US National Science Foundation Career Award IIS0747120.
PY - 2008
Y1 - 2008
N2 - With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 × 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
AB - With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 × 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.
KW - Internet images
KW - Large data sets
KW - Nearest neighbor methods
KW - Object recognition
KW - Tiny images
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U2 - 10.1109/TPAMI.2008.128
DO - 10.1109/TPAMI.2008.128
M3 - Article
C2 - 18787244
AN - SCOPUS:54749092170
SN - 0162-8828
VL - 30
SP - 1958
EP - 1970
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 11
ER -