• DocumentCode
    457313
  • Title

    Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces

  • Author

    Bhattacharya, Prabir ; Rahman, Md Mahmudur ; Desai, Bipin C.

  • Author_Institution
    Concordia University, Canada
  • Volume
    2
  • fYear
    2006
  • fDate
    20-24 Aug. 2006
  • Firstpage
    1162
  • Lastpage
    1168
  • Abstract
    This paper presents a learning based framework for content-based image retrieval to bridge the gap between low-level image features and high-level semantic information presented in the images on semantically organized collections. Both supervised (probabilistic multi-class support vector machine) and unsupervised (fuzzy c-means clustering) learning based techniques are investigated to associate global MPEG-7 based color and edge features with their high-level semantical and/or visual categories. It represents images in a successive semantic level of information abstraction based on confidence or membership scores obtained from the learning algorithms. A fusion-based similarity matching function is employed on these new image representations to rank and retrieve most similar images compared to a query image. Experimental results on a generic image database with manually assigned semantic categories and on a medical image database with different modalities and examined body parts demonstrate the effectiveness of the proposed approach compared to the commonly used Euclidean distance measure on MPEG-7 based descriptors.
  • Keywords
    Bridges; Clustering algorithms; Content based retrieval; Image databases; Image representation; Image retrieval; Information retrieval; MPEG 7 Standard; Machine learning; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-2521-0
  • Type

    conf

  • DOI
    10.1109/ICPR.2006.687
  • Filename
    1699416