A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Compressive Video Classification In A Low-Dimensional Manifold With Learned Distance Metric
2012
Zenodo
Preserved Fulltext
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 2012 ...
A distance metric learning approach followed the initial dimensionality reduction in the compressed domain, to learn the relative distances between the training samples in a supervised way by exploiting ...
We overcome the above drawbacks by employing a distance metric learning (DML) combined with a manifold learning (ML) approach. ...
doi:10.5281/zenodo.52363
fatcat:zvnof4sz6zfg3efqu6xl2be4ne
Multiple Manifolds Metric Learning with Application to Image Set Classification
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
A metric Learning method has been devised to embed these kernel spaces into a lower dimensional common subspace for classification. ...
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riemannian manifold. ...
Riemannian distance metric [11, 13, 10, 12] , and then learn a map to transform it to a low dimensional one. ...
arXiv:1805.11918v1
fatcat:yrfxbnvhunaczcsmbdhojol6zq
Projection Metric Learning on Grassmann Manifold with Application to Video based Face Recognition
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
To overcome such limitations, we propose a novel method to learn the Projection Metric directly on Grassmann manifold rather than in Hilbert space. ...
In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold. ...
a low-dimensional SPD manifold. ...
doi:10.1109/cvpr.2015.7298609
dblp:conf/cvpr/HuangWSC15
fatcat:xcnbavicpbfiva3rvhy6ppmuh4
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. ...
and metric learning on Grassmann manifold to improve performance. ...
Huang et al. in [1] proposed a Grassmann manifold dimensionality reduction method, which jointly performs the process of mapping and metric learning, and generates a low dimensional, more discriminative ...
arXiv:1806.10830v2
fatcat:odvxpnxpc5gy5h3mzkyjpnsrre
Viewpoint Manifolds for Action Recognition
2009
EURASIP Journal on Image and Video Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous ...
Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous ...
In [23] , a framework is presented for selecting image distance metrics for use with manifold learning. ...
doi:10.1155/2009/738702
fatcat:h4anyadp3be6rojbcbur4oy4pa
Hybrid Euclidean-and-Riemannian Metric Learning for Image Set Classification
[chapter]
2015
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
spaces into high dimensional Hilbert spaces and then jointly learn hybrid metrics with discriminant constraint. ...
We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. ...
It has 1,910 video clips of 47 subjects collected from YouTube. Most clips contains hundreds of frames, which are often low resolution and highly compressed with noise and low quality. ...
doi:10.1007/978-3-319-16811-1_37
fatcat:ihjhwmxo5rbglfwknfvbovj2jq
Probability Distribution-based Dimensionality Reduction on Riemannian Manifold of SPD Matrices
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Most video sequences are low resolution and recorded at a high compression ratio with different numbers of frames (vary from 8 to 400). ...
[2] and framebased video classification [15] . ...
In the future, we intend to extend our probability distribution-based approach to the unsupervised scenario. ...
doi:10.1109/access.2020.3017234
fatcat:w2brc7hqbfdqtgei63iseojz2a
A Compact and Discriminative Face Track Descriptor
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Our goal is to learn a compact, discriminative vector representation of a face track, suitable for the face recognition tasks of verification and classification. ...
Second, the descriptor is compact due to discriminative dimensionality reduction, and it can be further compressed using binarization. ...
Binary compression While the low-rank metric learning method of Sect. 3.2 is already capable of achieving a very good compression factor, for large scale applications the goal is to further decrease the ...
doi:10.1109/cvpr.2014.219
dblp:conf/cvpr/ParkhiSVZ14
fatcat:kefzq3qrv5f7bkjmebvxfwyktu
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
[chapter]
2016
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. ...
Moreover, FV best deals with moderate scale and translation changes. ...
The space of m-dimensional LS in R n can be viewed as a special case of Riemannian manifolds, known as Grassmann manifolds [47] . ...
doi:10.1007/978-3-319-42996-0_8
fatcat:yxeuj3sxnzf6rghujwi5l7mwiu
Event monitoring via local motion abnormality detection in non-linear subspace
2010
Neurocomputing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We use motion vectors extracted over a Region of Interest (ROI) as features and a non-linear, graph-based manifold learning algorithm coupled with a supervised novelty classifier to label segments of a ...
video sequence. ...
Using this metric, Isomap can achieve adequate results to learn (unfold) manifolds that have a non-linear global structure. ...
doi:10.1016/j.neucom.2009.10.028
fatcat:if6u5a3ag5ffvaegl2vdvxhzkm
A Framework for Short Video Recognition Based on Motion Estimation and Feature Curves on SPD Manifolds
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This paper presents a new short video recognition algorithm framework that transforms a short video into a family of feature curves on symmetric positive definite (SPD) manifold as the basis of recognition ...
A significant feature of short video is that there are few switches of scenes in short video, and the target (e.g., the face of the key person in the short video) often runs through the short video. ...
DALG DALG aims to learn a mapping that transforms a high-dimensional Lie group (LG) into a more discriminative, low-dimensional one. ...
doi:10.3390/app12094669
fatcat:52hnmqfw6zgkjezdiraeytouq4
Learning the viewpoint manifold for action recognition
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit) that can be used for video obtained from a single camera for simultaneous ...
Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available data set previously only acheieved using multiple ...
learning how the appearance of an action varies as the viewpoint changes by learning a low dimensional representation of action primitives using manifold learning. ...
doi:10.1109/cvpr.2008.4587552
dblp:conf/cvpr/SouvenirB08
fatcat:dvxc4hgrgffzxauhlpukba665y
Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning
2015
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
With a LogDet divergence based objective function, the hybrid kernels are then fused by our hybrid metric learning framework, which can efficiently perform the fusing procedure on large-scale videos. ...
In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. ...
Most clips contain hundreds of frames, which are often of low resolution and highly compressed with noise and low quality. ...
doi:10.1016/j.patcog.2015.03.011
fatcat:sqr2cvioajdczbfmno3mnfonha
Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, we aim to construct a deep neural network which embeds high dimensional symmetric positive definite (SPD) matrices into a more discriminative low dimensional SPD manifold. ...
Finally, we complete the construction of the deep neural network for SPD manifold learning by stacking the two layers. ...
First, learning discriminative representations in new learned low dimensional SPD space brings low computational cost. ...
doi:10.1609/aaai.v31i1.11232
fatcat:bfd24zkkrve27e2gw3td7vxixm
Some new directions in graph-based semi-supervised learning
2009
2009 IEEE International Conference on Multimedia and Expo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is application/pdf.
(ii) online semi-supervised learning that learns incrementally with low computation and memory needs; and (iii) learning spectrally sparse but non-smooth labels with compressive sensing. ...
is restricted to live on a single manifold; (2) learning must happen in batch mode; and (3) the target label is assumed smooth on the manifold. ...
In particular, Belkin et al. generalize graph-based learning to the manifold setting, where X is assumed to be a low dimensional manifold in R D , the labels y change smoothly on the manifold, and the ...
doi:10.1109/icme.2009.5202789
dblp:conf/icmcs/ZhuGK09
fatcat:2v5ss5iowfewhbb7w4a5ysiqti
« Previous
Showing results 1 — 15 out of 1,894 results