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Spatial and multi-resolution context in visual indexing

  • Jenny Benois-Pineau
  • Aurélie Bugeau
  • Svebor Karaman
  • Rémi Mégret
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Recent trends in visual indexing make appear a large family of methods which use a local image representation via descriptors associated to the interest points, see chapter 2. Such approaches mostly ”forget” any structure in the image considering unordered sets of descriptors or their histograms as image model. Hence, more advanced approaches try to overcome this drawback by adding spatial arrangements to the interest points. In this chapter we will present two trends in incorporation of spatial context into visual description, such as considering spatial context in the process of matching of signatures on one hand and design of structural descriptors which are then used in a global Bag-of-Visual-Words (BoVW) approach on the other hand. As images and video are mainly available in a compressed form, we shortly review global descriptors extracted from compressed stream and hence less sensible to compression artifacts. Furthermore, on the basis of scalable, multiresolution/ multi-scale visual content representation in modern compression standards, we study how this multi-resolution context can be efficiently incorporated into a BoVW approach.

Keywords

Visual Word Interest Point Spatial Context Mean Average Precision Spatial Pyramid Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Jenny Benois-Pineau
    • 1
  • Aurélie Bugeau
  • Svebor Karaman
  • Rémi Mégret
  1. 1.Univ. Bordeaux LABRI UMR 5800TalenceFrance

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