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Object Detection Combining Recognition and Segmentation

  • Liming Wang
  • Jianbo Shi
  • Gang Song
  • I-fan Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to generate a set of hypotheses of object locations and figure-ground masks, which have high recall and low precision rate. In the verification step, we first compute a set of feasible segmentations that are consistent with top-down object hypotheses, then we propose a False Positive Pruning(FPP) procedure to prune out false positives. We exploit the fact that false positive regions typically do not align with any feasible image segmentation. Experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many object classes.

Keywords

Object Detection Object Class Background Clutter Pedestrian Detection Mask Function 
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 2007

Authors and Affiliations

  • Liming Wang
    • 1
  • Jianbo Shi
    • 2
  • Gang Song
    • 2
  • I-fan Shen
    • 1
  1. 1.Fudan University,Shanghai, 200433PRC
  2. 2.University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA 19104 

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