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Mixing Hierarchical Contexts for Object Recognition

  • Billy Peralta
  • Alvaro Soto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

Robust category-level object recognition is currently a major goal for the Computer Vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information in the form of object co-ocurrences and spatial contraints has been successfully applied to reduce the inherent uncertainty of the visual world. Recently, Choi et al. [5] propose the use of a tree-structured graphical model to capture contextual relations among objects. Under this model there is only one possible fixed contextual relation among subsets of objects. In this work we extent Choi et al. approach by using a mixture model to consider the case that contextual relations among objects depend on scene type. Our experiments highlight the advantages of our proposal, showing that the adaptive specialization of contextual relations improves object recognition and object detection performances.

Keywords

Object Recognition Object Detection Conditional Random Field Contextual Relation Computer Vision Community 
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

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Billy Peralta
    • 1
  • Alvaro Soto
    • 1
  1. 1.Pontificia Universidad Católica de ChileChile

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