Improvement of Bag of Visual Words Using Iconclass

DOI

説明

Recently, bag-of-visual-words has been studied as an image retrieval approach that uses the defining features of images. However, k-means clustering, generally used in bag-of-visual-words, has a drawback in that its results are affected by setting initial points and their number. Additionally, the more the number of keypoints increases, the more expensive processing becomes. We solve these problems of bag-of-visual-words by using a quantizing method that we have developed. In addition, we have developed a theme comprehending system that uses ontology.

収録刊行物

  • SCIS & ISIS

    SCIS & ISIS 2010 (0), 142-145, 2010

    日本知能情報ファジィ学会

キーワード

詳細情報 詳細情報について

  • CRID
    1390282680565873152
  • NII論文ID
    130005019504
  • DOI
    10.14864/softscis.2010.0.142.0
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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