From bikers to surfers: Visual recognition of Urban tribes
Research output: Contribution to conference › Paper › Research › peer-review
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From bikers to surfers : Visual recognition of Urban tribes. / Kwak, Iljung S.; Murillo, Ana C.; Belhumeur, Peter N.; Kriegman, David; Belongie, Serge.
2013. Paper presented at 2013 24th British Machine Vision Conference, BMVC 2013, Bristol, United Kingdom.Research output: Contribution to conference › Paper › Research › peer-review
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TY - CONF
T1 - From bikers to surfers
T2 - 2013 24th British Machine Vision Conference, BMVC 2013
AU - Kwak, Iljung S.
AU - Murillo, Ana C.
AU - Belhumeur, Peter N.
AU - Kriegman, David
AU - Belongie, Serge
PY - 2013
Y1 - 2013
N2 - The terms Biker, Punk, Hipster, Goth or Surfer often spark visual depictions of individuals with very distinct fashion styles. These visually salient styles can provide insight into the social identity of an individual. However, despite its potential usefulness, little work has been done to automatically classify images of people into social categories. We tackle this problem by analyzing pictures of groups of individuals and creating models to represent them. We capture the features that distinguish each subculture and show promising results for automatic classification. This work gives vision algorithms access to the social identity of an individual and helps improve the quality of socially motivated image search, relevance of advertisements, and recommendations of social groups.
AB - The terms Biker, Punk, Hipster, Goth or Surfer often spark visual depictions of individuals with very distinct fashion styles. These visually salient styles can provide insight into the social identity of an individual. However, despite its potential usefulness, little work has been done to automatically classify images of people into social categories. We tackle this problem by analyzing pictures of groups of individuals and creating models to represent them. We capture the features that distinguish each subculture and show promising results for automatic classification. This work gives vision algorithms access to the social identity of an individual and helps improve the quality of socially motivated image search, relevance of advertisements, and recommendations of social groups.
UR - http://www.scopus.com/inward/record.url?scp=84898408833&partnerID=8YFLogxK
U2 - 10.5244/C.27.14
DO - 10.5244/C.27.14
M3 - Paper
AN - SCOPUS:84898408833
Y2 - 9 September 2013 through 13 September 2013
ER -
ID: 302046576