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      • Department of Computer Engineering
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      Toward an estimation of user tagging credibility for social image retrieval

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      Author
      Ginsca, A. L.
      Popescu, A.
      Ionescu, B.
      Armağan, Anıl
      Kanellos, I.
      Date
      2014-11
      Source Title
      MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
      Publisher
      ACM
      Pages
      1021 - 1024
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Existing image retrieval systems exploit textual or/and visual information to return results. Retrieval is mostly focused on data themselves and disregards the data sources. In Web 2.0 platforms, the quality of annotations provided by different users can vary strongly. To account for this variability, we complement existing methods by introducing user tagging credibility in the retrieval process. Tagging credibility is automatically estimated by leveraging a large set of visual concept classifiers learned with Overfeat, a convolutional neural network (CNN) feature. A good image retrieval system should return results that are both relevant and diversified and here we tackle both challenges. Classically, we diversify results by using a k-Means algorithm and increase relevance by favoring images uploaded by users with good credibility estimates. Evaluation is performed on DIV400, a publicly available social image retrieval dataset and shows that our method is competitive with existing approaches.
      Keywords
      Image retrieval
      Neural networks
      User interfaces
      Convolutional neural network
      Data-sources
      Image retrieval systems
      k-Means algorithm
      Retrieval process
      Social image retrievals
      Visual concept
      Visual information
      Search engines
      Permalink
      http://hdl.handle.net/11693/27741
      Published Version (Please cite this version)
      https://doi.org/10.1145/2647868.2655033
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      • Department of Computer Engineering 1368
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