Designing a boosted classifier on riemannian manifolds

Fatih Porikli, Oncel Tuzel, Peter Meer

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations


It is not trivial to build a classifier where the domain is the space of symmetric positive definite matrices such as non-singular region covariance descriptors lying on a Riemannian manifold. This chapter describes a boosted classification approach that incorporates the a priori knowledge of the geometry of the Riemannian space. The presented classifier incorporated into a rejection cascade and applied to single image human detection task. Results on INRIA and DaimlerChrysler pedestrian datasets are reported.

Original languageEnglish (US)
Title of host publicationRiemannian Computing in Computer Vision
PublisherSpringer International Publishing
Number of pages21
ISBN (Electronic)9783319229577
ISBN (Print)9783319229560
StatePublished - Jan 1 2015

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science(all)
  • Mathematics(all)


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