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.
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Porikli, F., Tuzel, O., Meer, P. (2016). Designing a Boosted Classifier on Riemannian Manifolds. In: Turaga, P., Srivastava, A. (eds) Riemannian Computing in Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-319-22957-7_13
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DOI: https://doi.org/10.1007/978-3-319-22957-7_13
Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-22957-7
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