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Contextual Object Detection Using Set-Based Classification

  • Ramazan Gokberk Cinbis
  • Stan Sclaroff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

We propose a new model for object detection that is based on set representations of the contextual elements. In this formulation, relative spatial locations and relative scores between pairs of detections are considered as sets of unordered items. Directly training classification models on sets of unordered items, where each set can have varying cardinality can be difficult. In order to overcome this problem, we propose SetBoost, a discriminative learning algorithm for building set classifiers. The SetBoost classifiers are trained to rescore detected objects based on object-object and object-scene context. Our method is able to discover composite relationships, as well as intra-class and inter-class spatial relationships between objects. The experimental evidence shows that our set-based formulation performs comparable to or better than existing contextual methods on the SUN and the VOC 2007 benchmark datasets.

Keywords

Average Precision Object Class Context Model Reference Object Contextual Relationship 
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 2012

Authors and Affiliations

  • Ramazan Gokberk Cinbis
    • 1
  • Stan Sclaroff
    • 2
  1. 1.LEAR, INRIA GrenobleFrance
  2. 2.Department of Computer ScienceBoston UniversityUSA

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