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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tuzel, O. Porikli, F. Meer, P. |
| Copyright Year | 2007 |
| Description | Author affiliation: Rutgers Univ., Piscataway (Tuzel, O.) |
| Abstract | We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known machine learning techniques are not adequate to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. We present a novel approach for classifying points lying on a Riemannian manifold by incorporating the a priori information about the geometry of the space. The algorithm is tested on INRIA human database where superior detection rates are observed over the previous approaches. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 2332090 |
| Page Count | 8 |
| File Format | |
| ISBN | 1424411793 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2007.383197 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Covariance matrix Object detection Manifolds Support vector machines Support vector machine classification Machine learning Spatial databases Detectors Histograms |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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