• DocumentCode
    2476549
  • Title

    Novel image feature alphabets for object recognition

  • Author

    Lillholm, Martin ; Griffin, Lewis

  • Author_Institution
    Dept. of Comput. Sci., Univ. Coll. London, London, UK
  • fYear
    2008
  • fDate
    8-11 Dec. 2008
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Most successful object recognition systems are based on a visual alphabet of quantised gradient orientations. Here, we introduce two richer image feature alphabets for use in object recognition. The two alphabets are evaluated using the PASCAL VOC challenge 2007 dataset. The results show that both alphabets perform as well as or better than the ´standard´ gradient orientation based one.
  • Keywords
    feature extraction; gradient methods; object recognition; quantisation (signal); PASCAL VOC 2007 dataset; image feature alphabet; object recognition; quantised gradient orientation; visual alphabet; Computer science; Educational institutions; Encoding; Image edge detection; Labeling; Object recognition; Pipelines; Pixel; Quantization; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
  • Conference_Location
    Tampa, FL
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-2174-9
  • Electronic_ISBN
    1051-4651
  • Type

    conf

  • DOI
    10.1109/ICPR.2008.4761173
  • Filename
    4761173
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2476549