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Article overview
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Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval | Xirong Li
; Tiberio Uricchio
; Lamberto Ballan
; Marco Bertini
; Cees G. M. Snoek
; Alberto Del Bimbo
; | Date: |
28 Mar 2015 | Abstract: | Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image. By analyzing what information a specific method exploits to
construct its tag relevance function and how such information is exploited,
this paper introduces a taxonomy to structure the growing literature,
understand the ingredients of the main works, clarify their connections and
difference, and recognize their merits and limitations. For a head-to-head
comparison between the state-of-the-art, a new experimental protocol is
presented, with training sets containing 10k, 100k and 1m images and an
evaluation on three test sets, contributed by various research groups. Eleven
representative works are implemented and evaluated. Putting all this together,
the survey aims to provide an overview of the past and foster progress for the
near future. | Source: | arXiv, 1503.8248 | Services: | Forum | Review | PDF | Favorites |
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