Abstract
Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their behaviors may be application dependent, raising the need of manual selection to achieve the best possible performance. Further and as a result of their overwhelming complexity for large-scale problems, computing pairwise similarities by clever embedding of SPD matrices is often preferred to direct use of the aforementioned measures. In this paper, we propose a discriminative metric learning framework, Information Divergence and Dictionary Learning (IDDL), that not only learns application specific measures on SPD matrices automatically, but also embeds them as vectors using a learned dictionary. To learn the similarity measures (which could potentially be distinct for every dictionary atom), we use the recently introduced αß-logdet divergence, which is known to unify the measures listed above. We propose a novel IDDL objective, that learns the parameters of the divergence and the dictionary atoms jointly in a discriminative setup and is solved efficiently using Riemannian optimization. We showcase extensive experiments on eight computer vision datasets, demonstrating state-of-the-art performances.
Original language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4280-4289 |
Number of pages | 10 |
ISBN (Electronic) | 9781538610329 |
DOIs | |
State | Published - Dec 22 2017 |
Event | 16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy Duration: Oct 22 2017 → Oct 29 2017 |
Publication series
Name | Proceedings of the IEEE International Conference on Computer Vision |
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Volume | 2017-October |
ISSN (Print) | 1550-5499 |
Other
Other | 16th IEEE International Conference on Computer Vision, ICCV 2017 |
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Country/Territory | Italy |
City | Venice |
Period | 10/22/17 → 10/29/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.