Voting by Grouping Dependent Parts

  • Pradeep Yarlagadda
  • Antonio Monroy
  • Björn Ommer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)


Hough voting methods efficiently handle the high complexity of multi-scale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes.


Training Image Object Detection Query Image Correspondence Problem Query Feature 
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 2010

Authors and Affiliations

  • Pradeep Yarlagadda
    • 1
  • Antonio Monroy
    • 1
  • Björn Ommer
    • 1
  1. 1.Interdisciplinary Center for Scientific ComputingUniversity of HeidelbergGermany

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