@techreport{zam-sto-09-musis-tr,
  institution = {Institute of Computing, UNICAMP},
  number = {IC-09-39},
  title = {Image Retrieval by Multi-Scale Interval Distance Estimation},
  author = {Carlos Elias Arminio Zampieri and Jorge Stolfi},
  month = oct,
  year = 2009,
  pages = {11},
  abstract = {We describe a general method for query-by-example retrieval in image collections, using interval arithmetic to perform multi-scale distance estimation. The interval estimates are used to quickly eliminate candidate images at small scales, in a fashion similar to the branch-and-bound optimization paradigm. Experiments indicate that the method can provide significant speedup relative to exhaustive search; nevertheless, the method always returns the exact best match (and not merely an approximation thereof). The technique allows queries with a wide variety of image similarity functions, without the need to precompute or store specific descriptors for each function.}
}