TY - GEN
T1 - Approximate infinite-dimensional Region Covariance Descriptors for image classification
AU - Faraki, Masoud
AU - Harandi, Mehrtash T.
AU - Porikli, Fatih
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/4
Y1 - 2015/8/4
N2 - We introduce methods to estimate infinite-dimensional Region Covariance Descriptors (RCovDs) by exploiting two feature mappings, namely random Fourier features and the Nyström method. In general, infinite-dimensional RCovDs offer better discriminatory power over their low-dimensional counterparts. However, the underlying Riemannian structure, i.e., the manifold of Symmetric Positive Definite (SPD) matrices, is out of reach to great extent for infinite-dimensional RCovDs. To overcome this difficulty, we propose to approximate the infinite-dimensional RCovDs by making use of the aforementioned explicit mappings. We will empirically show that the proposed finite-dimensional approximations of infinite-dimensional RCovDs consistently outperform the low-dimensional RCovDs for image classification task, while enjoying the Riemannian structure of the SPD manifolds. Moreover, our methods achieve the state-of-the-art performance on three different image classification tasks.
AB - We introduce methods to estimate infinite-dimensional Region Covariance Descriptors (RCovDs) by exploiting two feature mappings, namely random Fourier features and the Nyström method. In general, infinite-dimensional RCovDs offer better discriminatory power over their low-dimensional counterparts. However, the underlying Riemannian structure, i.e., the manifold of Symmetric Positive Definite (SPD) matrices, is out of reach to great extent for infinite-dimensional RCovDs. To overcome this difficulty, we propose to approximate the infinite-dimensional RCovDs by making use of the aforementioned explicit mappings. We will empirically show that the proposed finite-dimensional approximations of infinite-dimensional RCovDs consistently outperform the low-dimensional RCovDs for image classification task, while enjoying the Riemannian structure of the SPD manifolds. Moreover, our methods achieve the state-of-the-art performance on three different image classification tasks.
KW - Region Covariance Descriptor
KW - Reproducing Kernel Hilbert Space
KW - Riemannian Geometry
UR - http://www.scopus.com/inward/record.url?scp=84946014376&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2015.7178193
DO - 10.1109/ICASSP.2015.7178193
M3 - Conference contribution
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1364
EP - 1368
BT - 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
Y2 - 19 April 2014 through 24 April 2014
ER -