Boosted Fractal Integral Paths for Object Detection

  • Arne Ehlers
  • Florian Baumann
  • Bodo Rosenhahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)


In boosting-based object detectors, weak classifiers are often build on Haar-like features using conventional integral images. That approach leads to the utilization of simple rectangle-shaped structures which are only partial suitable for curved-shaped structures, as present in natural object classes such as faces. In this paper, we propose a new class of fractal features based on space-filling curves, a special type of fractals also known as Peano curves. Our method incorporates the new feature class by computing integral images along these curves. Therefore space-filling curves offer our proposed features to describe a wider variety of shapes including self-similar structures. By introducing two subtypes of fractal features, three-point and four-point features, we get a richer representation of curved and topology separated but correlated structures. We compare AdaBoost using conventional Haar-like features and our proposed fractal feature class in several experiments on the well-known MIT+CMU upright face test set and a microscopy cell test set.


Object Detection True Positive Rate Fractal Feature Face Detection Integral Image 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Arne Ehlers
    • 1
  • Florian Baumann
    • 1
  • Bodo Rosenhahn
    • 1
  1. 1.Institut für Informationsverarbeitung (TNT)Leibniz Universität HannoverGermany

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