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    Large-Scale Image Annotation using Visual Synset

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    Date
    2011-11
    Author
    Tsai, David
    Jing, Yushi
    Liu, Yi
    Rowley, Henry A.
    Ioffe, Sergey
    Rehg, James M.
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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.
    URI
    http://hdl.handle.net/1853/44645
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    • Computational Perception & Robotics [213]
    • Computational Perception & Robotics Publications [213]

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