Designing a boosted classifier on riemannian manifolds

Fatih Porikli*, Oncel Tuzel, Peter Meer

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    6 Citations (Scopus)

    Abstract

    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
    Title of host publicationRiemannian Computing in Computer Vision
    PublisherSpringer International Publishing
    Pages281-301
    Number of pages21
    ISBN (Electronic)9783319229577
    ISBN (Print)9783319229560
    DOIs
    Publication statusPublished - 1 Jan 2015

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