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Backprojection Revisited: Scalable Multi-view Object Detection and Similarity Metrics for Detections

  • Nima Razavi
  • Juergen Gall
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

Hough transform based object detectors learn a mapping from the image domain to a Hough voting space. Within this space, object hypotheses are formed by local maxima. The votes contributing to a hypothesis are called support. In this work, we investigate the use of the support and its backprojection to the image domain for multi-view object detection. To this end, we create a shared codebook with training and matching complexities independent of the number of quantized views. We show that since backprojection encodes enough information about the viewpoint all views can be handled together. In our experiments, we demonstrate that superior accuracy and efficiency can be achieved in comparison to the popular one-vs-the-rest detectors by treating views jointly especially with few training examples and no view annotations. Furthermore, we go beyond the detection case and based on the support we introduce a part-based similarity measure between two arbitrary detections which naturally takes spatial relationships of parts into account and is insensitive to partial occlusions. We also show that backprojection can be used to efficiently measure the similarity of a detection to all training examples. Finally, we demonstrate how these metrics can be used to estimate continuous object parameters like human pose and object’s viewpoint. In our experiment, we achieve state-of-the-art performance for view-classification on the PASCAL VOC’06 dataset.

Keywords

Training Image Object Detection Image Domain Object Hypothesis Vote Space 
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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Supplementary material

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Nima Razavi
    • 1
  • Juergen Gall
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision LaboratoryETH Zurich 
  2. 2.ESAT-PSI/IBBTKU Leuven 

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