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      Ensemble of multiple instance classifiers for image re-ranking

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      Author(s)
      Sener F.
      Ikizler-Cinbis, N.
      Date
      2014
      Source Title
      Image and Vision Computing
      Print ISSN
      0262-8856
      Publisher
      Elsevier Ltd
      Volume
      32
      Issue
      5
      Pages
      348 - 362
      Language
      English
      Type
      Article
      Item Usage Stats
      179
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      Abstract
      Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate "multi-instance bags (MI-bags)", which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art. © 2014 Elsevier B.V.
      Keywords
      Image re-ranking
      Image retrieval
      Multiple Instance Learning
      Learning systems
      Image rankings
      Image re rankings
      Multiple instance learning
      Multiple instances
      Retrieval performance
      Text-based image retrievals
      User feedback
      Visual content
      Image retrieval
      Permalink
      http://hdl.handle.net/11693/26400
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.imavis.2014.02.014
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      • Department of Computer Engineering 1510
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