@InProceedings{Philbin-CVPR08,
  IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2008-12-23 },
  author = {Philbin, J. and Chum, O. and Isard, M. and 
            Sivic, J. and Zisserman, A.},
  title = {Lost in Quantization: Improving Particular Object 
           Retrieval in Large Scale Image Databases},
  booktitle = {CVPR 2008: Proceedings of the 2008 IEEE Computer Society
               Conference on Computer Vision and Pattern Recognition},
  year = {2008},
  authorship = {40-30-10-10-10},
  pages = {8},
  isbn = {978-1-4244-2243-2},
  issn = {1063-6919},
  book_pages =  {2954},
  publisher = {Omnipress},
  address = {Madison, USA},
  month = {June},
  day = {24-26},
  venue = {Anchorage, USA},
  organization ={IEEE Computer Society},
  project = {ICT-215078 DIPLECS},
  keywords = {soft assignment, image retrieval},
  annote = {The state of the art in visual object retrieval from large
    databases is achieved by systems that are inspired by text
    retrieval. A key component of these approaches is that local
    regions of images are characterized using high-dimensional
    descriptors which are then mapped to .visual words. selected from
    a discrete vocabulary.  This paper explores techniques to map each
    visual region to a weighted set of words, allowing the inclusion
    of features which were lost in the quantization stage of previous
    systems. The set of visual words is obtained by selecting words
    based on proximity in descriptor space. We describe how this
    representation may be incorporated into a standard tf-idf
    architecture, and how spatial verification is modified in the case
    of this soft-assignment.},
  note = {CD-ROM},
}