Learning discriminative αβ-divergences for positive definite matrices

A. Cherian, P. Stanitsas, M. Harandi, V. Morellas, N. Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

7 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
EditorsRita Cucchiara, Yasuyuki Matsushita, Nicu Sebe, Stefano Soatto
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4280-4289
Number of pages10
ISBN (Electronic)9781538610329
ISBN (Print)9781538610336
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Computer Vision 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
Conference number: 16th
http://iccv2017.thecvf.com/

Conference

ConferenceIEEE International Conference on Computer Vision 2017
Abbreviated titleICCV 2017
CountryItaly
CityVenice
Period22/10/1729/10/17
Internet address

Cite this

Cherian, A., Stanitsas, P., Harandi, M., Morellas, V., & Papanikolopoulos, N. (2017). Learning discriminative αβ-divergences for positive definite matrices. In R. Cucchiara, Y. Matsushita, N. Sebe, & S. Soatto (Eds.), Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 4280-4289). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCV.2017.458