• DocumentCode
    1564721
  • Title

    Co-Clustering Image Features and Semantic Concepts

  • Author

    Rege, Manjeet ; Ming Dong ; Fotouhi, Farshad

  • Author_Institution
    Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
  • fYear
    2006
  • Firstpage
    137
  • Lastpage
    140
  • Abstract
    In this paper, we present a novel idea of co-clustering image features and semantic concepts. We accomplish this by modelling user feedback logs and low-level features using a bipartite graph. Our experiments demonstrate that (1) incorporating semantic information achieves better image clustering and (2) feature selection in co-clustering narrows the semantic gap, thus enabling efficient image retrieval.
  • Keywords
    feature extraction; graph theory; image classification; pattern clustering; bipartite graph; coclustering image feature; image classification; image retrieval; semantic information; Bipartite graph; Clustering algorithms; Feedback; Image databases; Image retrieval; Machine vision; Multimedia databases; Multimedia systems; Pattern recognition; Spatial databases; clustering methods; feedback; graph theory; image classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2006 IEEE International Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1522-4880
  • Print_ISBN
    1-4244-0480-0
  • Type

    conf

  • DOI
    10.1109/ICIP.2006.312378
  • Filename
    4106485
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1564721