• DocumentCode
    2968213
  • Title

    Searching similar images — Vector quantization with S-tree

  • Author

    Platos, Jan ; Kromer, Pavel ; Snasel, Vaclav ; Abraham, Ajith

  • Author_Institution
    Dept. of Comput. Sci., VSB-Tech. Univ. of Ostrava, Ostrava, Czech Republic
  • fYear
    2012
  • fDate
    21-23 Nov. 2012
  • Firstpage
    384
  • Lastpage
    388
  • Abstract
    Searching of similar pictures was in the past based mainly on searching of similar picture names. We try to find an effective method how to search pictures by searching of similar information in the picture (histograms, shapes, blocks,). There already are some methods but still not effective enough. In this paper we describe a method where we combine vector quantization (VQ) and fuzzy S-trees. Work contains testing of our approach and you can see results in a final chapter of this paper. The benefit of this work is not the final solution but we put a key-stone for further research and for optimizations. First tests show up the efficiency and usefulness of our approach, which is under laid by executed tests.
  • Keywords
    fuzzy set theory; image retrieval; tree data structures; trees (mathematics); vector quantisation; VQ; fuzzy S-trees; image retrieval; modified data structure; similar image searching; similar information searching; similar picture name searching; vector quantization; Algorithm design and analysis; Clustering algorithms; Image coding; Image color analysis; Testing; Vector quantization; Vectors; fuzzy sets; image similarity; s-tree; vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Aspects of Social Networks (CASoN), 2012 Fourth International Conference on
  • Conference_Location
    Sao Carlos
  • Print_ISBN
    978-1-4673-4793-8
  • Type

    conf

  • DOI
    10.1109/CASoN.2012.6412433
  • Filename
    6412433