---
res:
  bibo_abstract:
  - Visual understanding is often based on measuring similarity between observations.
    Learning similarities specific to a certain perception task from a set of examples
    has been shown advantageous in various computer vision and pattern recognition
    problems. In many important applications, the data that one needs to compare come
    from different representations or modalities, and the similarity between such
    data operates on objects that may have different and often incommensurable structure
    and dimensionality. In this paper, we propose a framework for supervised similarity
    learning based on embedding the input data from two arbitrary spaces into the
    Hamming space. The mapping is expressed as a binary classification problem with
    positive and negative examples, and can be efficiently learned using boosting
    algorithms. The utility and efficiency of such a generic approach is demonstrated
    on several challenging applications including cross-representation shape retrieval
    and alignment of multi-modal medical images.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Michael M.
      foaf_name: Bronstein, Michael M.
      foaf_surname: Bronstein
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Bronstein, Alexander
      foaf_surname: Bronstein
      foaf_workInfoHomepage: http://www.librecat.org/personId=58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
    orcid: 0000-0001-9699-8730
  - foaf_Person:
      foaf_givenName: Fabrice
      foaf_name: Michel, Fabrice
      foaf_surname: Michel
  - foaf_Person:
      foaf_givenName: Nikos
      foaf_name: Paragios, Nikos
      foaf_surname: Paragios
  bibo_doi: 10.1109/cvpr.2010.5539928
  dct_date: 2010^xs_gYear
  dct_isPartOf:
  - 'http://id.crossref.org/issn/1063-6919 '
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Data fusion through cross-modality metric learning using similarity-sensitive
    hashing@
...