NSCKL: Normalized spectral clustering with kernel-based learning for semisupervised hyperspectral image classification
Spatial–spectral classification (SSC) has become a trend for hyperspectral image (HSI)
classification. However, most SSC methods mainly consider local information, so that some …
classification. However, most SSC methods mainly consider local information, so that some …
[图书][B] Statistical shape analysis: with applications in R
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 …
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 …
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 …
recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper …
Sliced Wasserstein kernels for probability distributions
Optimal transport distances, otherwise known as Wasserstein distances, have recently
drawn ample attention in computer vision and machine learning as powerful discrepancy …
drawn ample attention in computer vision and machine learning as powerful discrepancy …
Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification
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 …
bilinear pooled feature matrix of Convolutional Neural Network (CNN) has shown to achieve …
Geodesic exponential kernels: When curvature and linearity conflict
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 …
and positive results. First we show that the common Gaussian kernel can only be …
Multi-view clustering via deep concept factorization
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 …
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 …
learning applications such as image classification and word embeddings. However, working …
Person re-identification in aerial imagery
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 …
visual surveillance by utilizing the UAV platform has been very attractive. Most of the …