Large-Scale Image Annotation using Visual Synset
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Abstract
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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2011-11
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