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A Novel Multi-feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification
2020
IEEE Access
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Then, a multifeature joint sparse representation model(MF-JSR) is proposed by designing joint sparse constraints on the extracted features above. ...
To solve this problem, a novel fast multi-feature joint learning method(fMF-JLC) is proposed for PolSAR image classification. ...
To solve this problem, a multi-feature joint sparse representation method(MF-JSR) is proposed for PolSAR image classification. ...
doi:10.1109/access.2020.2973246
fatcat:4kbq6g2qqfeb7fnvvvs2kwcfvu
Improved Joint Sparse Models for Hyperspectral Image Classification Based on a Novel Neighbour Selection Strategy
2018
Remote Sensing
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Joint sparse representation has been widely used for hyperspectral image classification in recent years, however, the equal weight assigned to each neighbouring pixel is less realistic, especially for ...
In the report of [34], the authors proposed a multi-layer spatial-spectral sparse representation specifically for hyperspectral image classification. ...
Landgrebe from Purdue University for providing the free downloads of the hyperspectral AVIRIS dataset, Paolo Gamba from the Telecommunications and Remote Sensing Laboratory for providing the Pavia University ...
doi:10.3390/rs10060905
fatcat:yj5nhqq255c67otnvzrhib7nte
Visual Classification With Multitask Joint Sparse Representation
2012
IEEE Transactions on Image Processing
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A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. ...
Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their constructive comments on this paper. ...
doi:10.1109/tip.2012.2205006
pmid:22736645
fatcat:lt6lhmwy7zgufgi2bunp2aetfa
Visual classification with multi-task joint sparse representation
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. ...
Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their constructive comments on this paper. ...
doi:10.1109/cvpr.2010.5539967
dblp:conf/cvpr/YuanY10
fatcat:5in5q4wgjnhh7g7wuduq4zfj2u
Weighted joint sparse representation-based classification method for robust alignment-free face recognition
2015
Journal of Electronic Imaging (JEI)
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This work proposes a weighted joint sparse representation (WJSR)-based classification method for robust alignment-free face recognition, in which an image is represented by a set of scale-invariant feature ...
Published by SPIE under a Creative Commons Attribution 3.0 Unported License. ...
Xiaoming Zhu for helping us revise the organizational structure and grammar issues of the paper. ...
doi:10.1117/1.jei.24.1.013018
fatcat:acjsnnofzjhf5gtjachktjmley
Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition
2014
PLoS ONE
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Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. ...
In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. ...
Joint Dynamic Sparse Representation-based Classification (LCJDSRC) for local matching-based face recognition. ...
doi:10.1371/journal.pone.0113198
pmid:25419662
pmcid:PMC4242617
fatcat:4vjerkr2ibd2xjq4dyi4moq2ga
Hyperspectral Image Classification via Joint Sparse representation of Multi-layer Superpixles
2018
KSII Transactions on Internet and Information Systems
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In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. ...
Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. ...
superpixles for joint sparse representation classification. ...
doi:10.3837/tiis.2018.10.021
fatcat:3d7qwd2rifdw5fu3w45lr7tlkq
Multimodal Dictionary Learning and Joint Sparse Representation for HEp-2 Cell Classification
[chapter]
2015
Lecture Notes in Computer Science
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First, we propose a new framework for multi-modal fusion at the feature level. ...
Use of automatic classification for Indirect Immunofluorescence (IIF) images of HEp-2 cells is increasingly gaining interest in Antinuclear Autoantibodies (ANAs) detection. ...
Matching (SPM) [8] to provide the sparse representation of the input cell images. ...
doi:10.1007/978-3-319-24574-4_37
fatcat:d4azeamfmrfu5jfxirn3razhga
Multimodal sparse representation learning and applications
[article]
2016
arXiv
pre-print
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In particular, we propose the use of joint dictionary learning technique for sparse coding and formulate the joint representation for concision, cross-modal representations (in case of a missing modality ...
The framework can model relationships at a higher level by forcing the shared sparse representation. ...
Feature Representation
Accuracy
(a) Sparse coding of images
(Figure 1a
Table 5 : 5 Classification performance for image-text classification on PhotoTweet dataset. ...
arXiv:1511.06238v3
fatcat:wt7cvmbpkrbrxbvxnyoydwtbqq
Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images
2018
Mathematical Problems in Engineering
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Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification ...
At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. ...
Figure 3 : 3 The illustration of joint sparse representation of two class.
Figure 4 : 4 Classification maps by different methods for the AVIRIS Indian Pines image with overall accuracies (OA in %). ...
doi:10.1155/2018/8264961
fatcat:jgbe2i2xf5au7b6tib3nuiosve
Joint Supervised Dictionary and Classifier Learning for Multi-view SAR Image Classification
2019
IEEE Access
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A new multi-view sparse representation classification (SRC) algorithm based on joint supervised dictionary and classifier learning (MSRC-JSDC) is proposed for synthetic aperture radar (SAR) image classification ...
Unlike most existing sparse representation methods for SAR image classification, MSRC-JSDC learns a supervised sparse model from training samples by utilizing sample label information, rather than directly ...
ACKNOWLEDGMENT The authors would like to appreciate the editor and all reviewers for their valuable suggestions and constructive comments. ...
doi:10.1109/access.2019.2953366
fatcat:3xnudh3hwrhl3iwkp4nknx3zf4
Learning component-level sparse representation using histogram information for image classification
2011
2011 International Conference on Computer Vision
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level importance within one unified framework to give a discriminative representation for image groups. ...
In the end, by keeping the top K important components, a compact representation is derived for the sparse coding dictionary. ...
Acknowledgements This work was supported in part by National Science Council of Taiwan under the grant NSC 99-2220-E-007-016 and the SOC Joint Research Lab project sponsored by NO-VATEK. ...
doi:10.1109/iccv.2011.6126410
dblp:conf/iccv/ChiangDLC11
fatcat:wldncc4pxzhkjhwoqc7tvhb2v4
Exploring Inter-Instance Relationships within the Query Set for Robust Image Set Matching
2019
Sensors
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Some studies attempt to model query and gallery sets under a joint or collaborative representation framework, achieving impressive performance. ...
In this paper, inter-instance relationships within the query set are explored for robust image set matching. ...
The authors would also like to thank the authors of the methods used for comparisons who provide public data and the anonymous reviewers who provide extensive constructive comments that improved our work ...
doi:10.3390/s19225051
pmid:31752415
pmcid:PMC6891765
fatcat:bhrds2gf5bgv3m74vdkamfyswa
Joint sparse representation for video-based face recognition
2014
Neurocomputing
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In this paper, we consider face images from each clip as an ensemble and formulate VFR into the Joint Sparse Representation (JSR) problem. ...
Video-based Face Recognition (VFR) can be converted into the problem of measuring the similarity of two image sets, where the examples from a video clip construct one image set. ...
Given a test image set (or a video clip), the group-level (or class-level) sparse recovery is used to search the most relevant subjects from gallery image sets (or gallery clips), while the atom-level ...
doi:10.1016/j.neucom.2013.12.004
fatcat:oh3so3veunbwhg7ahgv2vz3ojq
Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification
2019
Remote Sensing
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By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and ...
better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. ...
Landgrebe for providing the Indian Pines data set and J. Anthony Gualtieri for providing the Salinas data set.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs11091114
fatcat:7qpcgbtmcfgfxpjbs4aziiednu
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