• DocumentCode
    2972472
  • Title

    Image retrieval using the longest approximate common subsequences

  • Author

    Wen-Chen Hu ; Schmalz, Mark S. ; Ritter, Gerhard X.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Auburn Univ., AL
  • Volume
    2
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    730
  • Abstract
    Accurate and fast retrieval as well as efficient storage are two principal needs in an image database. Image search methods are the crucial factors in fulfilling these requirements. The longest common subsequence search method is mostly used in string-based image databases. However, a longest common subsequence does not always reveal the degree of difference between two strings. This research proposes a new image matching method, i.e., finding a longest approximate common subsequence of two strings. The experimental results show any image in the system can be precisely retrieved as long as each image has a unique image string, where accuracy of image retrieval depends on the level of query detail
  • Keywords
    image matching; image retrieval; visual databases; image database; image matching; image retrieval; image search methods; longest approximate common subsequences; longest common subsequence search method; Computer science; Data engineering; Image databases; Image representation; Image retrieval; Image storage; Information retrieval; Los Angeles Council; Neural networks; Search methods;
  • 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.1999.778575
  • Filename
    778575