Porikli, Fatih; Tuzel, Oncel; Meer, Peter Designing a boosted classifier on Riemannian manifolds. (English) Zbl 1359.94040 Turaga, Pavan K. (ed.) et al., Riemannian computing in computer vision. Cham: Springer (ISBN 978-3-319-22956-0/hbk; 978-3-319-22957-7/ebook). 281-301 (2016). Summary: 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.For the entire collection see [Zbl 1335.65003]. Cited in 7 Documents MSC: 94A08 Image processing (compression, reconstruction, etc.) in information and communication theory 53C15 General geometric structures on manifolds (almost complex, almost product structures, etc.) 62H11 Directional data; spatial statistics PDFBibTeX XMLCite \textit{F. Porikli} et al., in: Riemannian computing in computer vision. Cham: Springer. 281--301 (2016; Zbl 1359.94040) Full Text: DOI References: This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.