Large-Scale Image Annotation using Visual Synset

(ICCV 2011)

Illustration of Visual Synsets


David Tsai
Yushi Jing
Yi Liu
Henry A.Rowley
Sergey Ioffe
James M.Rehg

We address the problem of large-scale annotation of
web images. Our approach is based on the concept of
visual synset, which is an organization of images which
are visually-similar and semantically-related. Each visual
synset represents a single prototypical visual concept, and
has an associated set of weighted annotations. Linear
SVM’s are utilized to predict the visual synset membership
for unseen image examples, and a weighted voting rule is
used to construct a ranked list of predicted annotations from
a set of visual synsets. We demonstrate that visual synsets
lead to better performance than standard methods on a new
annotation database containing more than 200 million im-
ages and 300 thousand annotations, which is the largest
ever reported.

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   author = {David Tsai and Yushi Jing and Yi Liu and Henry A.Rowley and Sergey Ioffe and James M.Rehg},
   title = {Large-Scale Image Annotation using Visual Synset},
   journal = {ICCV},
   year = {2011},
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This research is supported by:
  • NSF Grant 0960618
  • Google Research

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