Object Detection by Admissible Region Search

  • Xiaoming Chen
  • Senjian An
  • Wanquan Liu
  • Wanqing Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

Efficient Subwindow Search(ESS) is an effective method for object detection and localization, which adopts a scheme of branch-and-bound to find the global optimum of a quality function from all the possible subimages. Since the number of possible subimage is \(\emph{O}(\emph{n}^{4})\) for an images with \(\emph{n}\times\emph{n}\) resolution, the time complexity of ESS ranges from \(\emph{O}(\emph{n}^{2})\) to \(\emph{O}(\emph{n}^{4})\). In other words, ESS is equivalent to the exhaustive search in the worst case. In this paper, we propose a new method named Adimissible Region Search(ARS) for detecting and localizing the object with arbitrary shape in an image. Compared with the sliding window methods using ESS, ARS has two advantages: firstly, the time complexity is quadratic and stable so that it is more suitable to process large resolution images; secondly, the admissible region is adaptable to match the real shape of the target object and thus more suitable to represent the object. The experimental results on PASCAL VOC 2006 demonstrate that the proposed method is much faster than the ESS method on average.

Keywords

Object Detection Search Time Quality Function Admissible Region Slide Window Method 
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

  • Xiaoming Chen
    • 1
  • Senjian An
    • 1
  • Wanquan Liu
    • 1
  • Wanqing Li
    • 2
  1. 1.Department of ComputingCurtin UniversityPerthAustralia
  2. 2.Information and Communication Technology (ICT) Research InstituteUniversity of WollongongAustralia

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