We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/∼calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches. © 2011. The copyright of this document resides with its authors.

A large-scale database of images and captions for automatic face naming / Özcan, Mert; Jie, Luo; Ferrari, Vittorio; Caputo, Barbara. - STAMPA. - (2011). (Intervento presentato al convegno 2011 22nd British Machine Vision Conference, BMVC 2011 tenutosi a Dundee; UK nel 29 August- 02 September 2011) [10.5244/C25.29].

A large-scale database of images and captions for automatic face naming

CAPUTO, BARBARA
2011

Abstract

We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/∼calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches. © 2011. The copyright of this document resides with its authors.
2011
2011 22nd British Machine Vision Conference, BMVC 2011
1707
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A large-scale database of images and captions for automatic face naming / Özcan, Mert; Jie, Luo; Ferrari, Vittorio; Caputo, Barbara. - STAMPA. - (2011). (Intervento presentato al convegno 2011 22nd British Machine Vision Conference, BMVC 2011 tenutosi a Dundee; UK nel 29 August- 02 September 2011) [10.5244/C25.29].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/951697
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