Abstract
Data representations based on Symmetric Positive Definite (SPD) matrices are gaining popularity in visual learning applications. When comparing SPD matrices, measures based on non-linear geometries often yield beneficial results. However, a manual selection process is commonly used to identify the appropriate measure for a visual learning application. In this paper, we study the problem of clustering SPD matrices while automatically learning a suitable measure. We propose a novel formulation that jointly (i) clusters the input SPD matrices in a K-Means setup and (ii) learns a suitable non-linear measure for comparing SPD matrices. For (ii), we capitalize on the recently introduced αβ-logdet divergence, which generalizes a family of popular similarity measures on SPD matrices. Our formulation is cast in a Riemannian optimization framework and solved using a conjugate gradient scheme. We present experiments on five computer vision datasets and demonstrate state-of-the-art performance.
Original language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1304-1312 |
Number of pages | 9 |
ISBN (Electronic) | 9781538610343 |
DOIs | |
State | Published - Jul 1 2017 |
Event | 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy Duration: Oct 22 2017 → Oct 29 2017 |
Publication series
Name | Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
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Volume | 2018-January |
Other
Other | 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 |
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Country/Territory | Italy |
City | Venice |
Period | 10/22/17 → 10/29/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.