IS = { zkontrolovano 01 Feb 2010 },
  UPDATE  = { 2010-01-08 },
  author = {Per{\v d}och, Michal and Chum, Ond{\v r}ej and Matas, Ji{\v r}{\' i}},
  title = {Efficient Representation of Local Geometry for Large Scale Object
  booktitle =   {CVPR 2009: Proceedings of the 2009 IEEE Computer
                 Society Conference on Computer Vision and Pattern Recognition},
  language = {english},
  year = {2009},
  pages = {9--16},
  month = {June},
  annote = {State of the art methods for image and object retrieval exploit both
	appearance (via visual words) and local geometry (spatial extent,
	relative pose). In large scale problems, memory becomes a limiting
	factor - local geometry is stored for each feature detected in each
	image and requires storage larger than the inverted file and term
	frequency and inverted document frequency weights together. We propose
	a novel method for learning discretized local geometry representation
	based on minimization of average reprojection error in the space
	of ellipses. The representation requires only 24 bits per feature
	without drop in performance. Additionally, we show that if the gravity
	vector assumption is used consistently from the feature description
	to spatial verification, it improves retrieval performance and decreases
	the memory footprint. The proposed method outperforms state of the
	art retrieval algorithms in a standard image retrieval benchmark.},
  keywords = {large scale object retrieval, geometry representation, geometric vocabulary},
  day = {20--25},
  publisher =   {Omnipress},
  address =     {Madison, USA},
  prestige =    {important},
  isbn = {978-1-4244-3991-1},
  issn = {1063-6919},
  book_pages =  {3000},
  venue = {Miami, USA},
  project = {GACR 102/07/1317, FP6-IST-027113},
  psurl  = {http://cmp.felk.cvut.cz/~perdom1/papers/cvpr09b.pdf},
  note = {CD-ROM},