• DocumentCode
    2832738
  • Title

    Quadtree classified vector quantization based image retrieval scheme

  • Author

    Chen, Hsin-Hui ; Sheu, Hsin-Teng ; Ding, Jian-Jiun

  • Author_Institution
    Grad. Inst. of Commun. Eng., Nat. Taiwan Univ., Taipei, Taiwan
  • fYear
    2011
  • fDate
    11-14 Sept. 2011
  • Firstpage
    3625
  • Lastpage
    3628
  • Abstract
    With the fast development of multimedia, it is crucial to find the way to search image database effectively. The vector quantization (VQ) based image retrieval method is popular in recent years. In this paper, we propose the quadtree classified vector quantization (QCVQ) scheme to improve the VQ method by exploiting the visual importance of image blocks and using the edge information to describe the content of each block efficiently. Moreover, we also apply the adaptive block size. The simulation results show that, compared with the previous image retrieval algorithms using VQ and chromaticity moments (CM), our proposed scheme has obviously better average retrieval rate and higher average precision.
  • Keywords
    image coding; image retrieval; trees (mathematics); vector quantisation; adaptive block size; chromaticity moments; image database searching; image retrieval scheme; multimedia; quadtree classified vector quantization; Histograms; Image coding; Image edge detection; Image retrieval; Image segmentation; Vector quantization; content based retrieval; image databases; image retrieval; multimedia databases; vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2011 18th IEEE International Conference on
  • Conference_Location
    Brussels
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4577-1304-0
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2011.6116503
  • Filename
    6116503