ICCV 2017 Tutorial

Covariance 2017

Venice - Sunday October 22

Publications

Publications by the organizer

• (Book) Hà Quang Minh and Vittorio Murino
  Covariances in Computer Vision and Machine Learning
  Morgan & Claypool Synthesis Lectures on Computer Vision, October 2017
  Online link at this link
• Hà Quang Minh
  Infinite-dimensional Log-Determinant divergences between positive definite trace class operators
  Linear Algebra and Its Applications, volume 528, pages 331-383, 2017, available online at this link
• Hà Quang Minh
  Log-Determinant divergences between positive definite Hilbert-Schmidt operators
  Geometric Science of Information (GSI 2017), available online at this link
• (Book) Hà Quang Minh and Vittorio Murino [editors]
  Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization
  Springer series in Advances in Computer Vision and Pattern Recognition, 2016
  Available online at this link
• Hà Quang Minh and Vittorio Murino
  From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings
  In Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization
  Springer series in Advances in Computer Vision and Pattern Recognition, 2016
  Available online at this link
• Hà Quang Minh, Marco San Biagio, Loris Bazzani, and Vittorio Murino
  Approximate Log-Hilbert-Schmidt distances between covariance operators for image classification
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), Las Vegas, Nevada, USA, June 2016
  Available online at this link
• Hà Quang Minh, Loris Bazzani, and Vittorio Murino
  A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi- view Learning
  Journal of Machine Learning Research, 17(25):1-72, 2016
  Available online at this link
• Hà Quang Minh
  Affine-invariant Riemannian Distance Between Infinite-dimensional Covariance Operators
  Geometric Science of Information (GSI 2015), Paris-Saclay, France, October 2015
  Available online at this link
• L. Dodero, Hà Quang Minh, M. San Biagio, V. Murino and D. Sona
  Kernel-based Classification For Brain Connectivity Graphs On The Riemannian Manifold Of Positive Definite Matrices. Proceedings of the International Symposium on Biomedical Imaging (ISBI 2015), New York, USA, April 2015
  Available online at this link
• Hà Quang Minh, Marco San Biagio, and Vittorio Murino
  Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
  Advances in Neural Information Processing Systems (NIPS 2014), December 2014, Montreal, Canada
  Available online at this link

Related publications by other researchers

• V. Arsigny, P. Fillard, X. Pennec, and N. Ayache
  Geometric means in a novel vector space structure on symmetric positive-definite matrices
  SIMAX, 29(1), 2007
• A. Cherian, S. Sra, A. Banerjee, and N. Papanikolopoulos
  Jensen-Bregman LogDet divergence with application to efficient similarity search for covariance matrices
  PAMI, 35(9):2161-2174, 2013
• M. Faraki, M. Harandi, and F. Porikli
  Approximate infinite-dimensional region covariance descriptors for image classification
  In ICASSP, 2015
• M. Harandi, M. Salzmann, and F. Porikli
  Bregman divergences for infinite dimensional covariance matrices
  In CVPR, 2014
• T. Kato, T. Matsuzawa, J. Sese, R. Relator
  Stochastic Dykstra Algorithms for Metric Learning with Positive Definite Covariance Descriptors
  In ECCV, 2016
• S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi
  Kernel methods on Riemannian manifolds with Gaussian RBF kernels
  PAMI, vol. 37, no. 12, pp. 2464-2477, 2015
• T. Matsukawa, T. Okabe, E. Suzuki, Y. Sato
  Hierarchical Gaussian Descriptor for Person Re-Identification
  In CVPR, 2016
• M. Moakher and M. Zerai.
  The Riemannian geometry of the space of positive-definite matrices and its application to the regularization of positive-definite matrix-valued data.
  Journal of Mathematical Imaging and Vision, 2011.
• X. Pennec, P. Fillard, and N. Ayache
  A Riemannian framework for tensor computing
  IJCV, 66(1):41-66, 2006
• S. Sra
  A new metric on the manifold of kernel matrices with application to matrix geometric means.
  In NIPS, 2012
• D. Tosato, M. Spera, M. Cristani, and V. Murino
  Characterizing humans on Riemannian manifolds
  PAMI, vol. 35, no. 8, pp. 1972-1984, Aug 2013
• O. Tuzel, F. Porikli, and P. Meer
  Pedestrian detection via classification on Riemannian manifolds
  PAMI, vol. 30, no. 10, pp. 1713-1727, 2008
• Q. Wang, P. Li, W. Zuo, L. Zhang
  RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian With Application to Material Recognition
  In CVPR, 2016
• R. Wang, H. Guo, L. Davis, and Q. Dai
  Covariance discriminative learning: A natural and efficient approach to image set classification
  In CVPR, pages 2496-2503, June 2012
• Y. Wang, O. Camps, M. Sznaier, B. Solvas
  Jensen Bregman LogDet Divergence Optimal Filtering in the Manifold of Positive Definite Matrices
  In ECCV, 2016
• M. Yin, Y. Guo, J. Gao, Z. He, S. Xie
  Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
  In CVPR, 2016
• S. K. Zhou and R. Chellappa
  From sample similarity to ensemble similarity: Probabilistic distance measures in reproducing kernel Hilbert space
  PAMI, 28(6):917-929, 2006