NSCKL: Normalized spectral clustering with kernel-based learning for semisupervised hyperspectral image classification

Y Su, L Gao, M Jiang, A Plaza, X Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …

[图书][B] Statistical shape analysis: with applications in R

IL Dryden, KV Mardia - 2016 - books.google.com
A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …

Dimensionality reduction on SPD manifolds: The emergence of geometry-aware methods

M Harandi, M Salzmann… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Representing images and videos with Symmetric Positive Definite (SPD) matrices, and
considering the Riemannian geometry of the resulting space, has been shown to yield high …

Building deep networks on grassmann manifolds

Z Huang, J Wu, L Van Gool - Proceedings of the AAAI Conference on …, 2018 - ojs.aaai.org
Learning representations on Grassmann manifolds is popular in quite a few visual
recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper …

Sliced Wasserstein kernels for probability distributions

S Kolouri, Y Zou, GK Rohde - Proceedings of the IEEE Conference …, 2016 - cv-foundation.org
Optimal transport distances, otherwise known as Wasserstein distances, have recently
drawn ample attention in computer vision and machine learning as powerful discrepancy …

Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification

X Wei, Y Zhang, Y Gong, J Zhang… - Proceedings of the …, 2018 - openaccess.thecvf.com
Designing discriminative and invariant features is the key to visual recognition. Recently, the
bilinear pooled feature matrix of Convolutional Neural Network (CNN) has shown to achieve …

Geodesic exponential kernels: When curvature and linearity conflict

A Feragen, F Lauze, S Hauberg - Proceedings of the IEEE …, 2015 - cv-foundation.org
We consider kernel methods on general geodesic metric spaces and provide both negative
and positive results. First we show that the common Gaussian kernel can only be …

Multi-view clustering via deep concept factorization

S Chang, J Hu, T Li, H Wang, B Peng - Knowledge-Based Systems, 2021 - Elsevier
Recent studies have shown the satisfactory results of the matrix factorization technique in
Multi-view Clustering (MVC). Compared with the single-layer formed clustering models, the …

Kernel methods in hyperbolic spaces

P Fang, M Harandi, L Petersson - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Embedding data in hyperbolic spaces has proven beneficial for many advanced machine
learning applications such as image classification and word embeddings. However, working …

Person re-identification in aerial imagery

S Zhang, Q Zhang, Y Yang, X Wei… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs),
visual surveillance by utilizing the UAV platform has been very attractive. Most of the …