• DocumentCode
    2985714
  • Title

    Relevance feedback techniques for image retrieval using multiple attributes

  • Author

    Chua, Tat-Seng ; Chu, Chun-Xin ; Kankanhalli, Mohan

  • Author_Institution
    Sch. of Comput., Nat. Univ. of Singapore, Singapore
  • Volume
    1
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    890
  • Abstract
    The paper proposes a relevance feedback (RF) approach to content based image retrieval using multiple attributes. The proposed approach has been applied to images´ text and color attributes. In order to ensure that meaningful features are extracted, a pseudo object model based on color coherence vector has been adopted to model color content. The RF approach employs techniques developed in the fields of information retrieval and machine learning to extract pertinent features from each of the attributes. It then uses the user´s relevance judgments to estimate the importance of different attributes in an integrated content based image retrieval. The system developed has been tested on a large image collection containing over 12000 images. The results demonstrate that the proposed RF approaches and pseudo object based color model are effective
  • Keywords
    content-based retrieval; feature extraction; image colour analysis; learning (artificial intelligence); relevance feedback; very large databases; visual databases; RF approach; color attributes; color coherence vector; color content; content based image retrieval; feature extraction; information retrieval; large image collection; machine learning; multiple attributes; pseudo object based color model; pseudo object model; relevance feedback techniques; relevance judgments; Coherence; Content based retrieval; Data mining; Feature extraction; Feedback; Image retrieval; Information retrieval; Machine learning; Radio frequency; System testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia Computing and Systems, 1999. IEEE International Conference on
  • Conference_Location
    Florence
  • Print_ISBN
    0-7695-0253-9
  • Type

    conf

  • DOI
    10.1109/MMCS.1999.779320
  • Filename
    779320