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Machine Learning Paradigm towards Content Based Image Retrieval on High Resolution Satellite Images
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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In this paper, SVM based on random subspace and boosting ensemble learning is proposed for very high resolution satellite image retrieval. ...
In order to retrieve high resolution satellite images Support Vector Machine (SVM) a machine learning paradigm is helpful for learning process and for pattern recognition and classification; ensemble methods ...
Partitioning the query image into data points using random subspace develop the performance results. Every sub-classifier is learned comparable image subspace. ...
doi:10.35940/ijitee.b1104.1292s219
fatcat:phvtsg6kmnd45ebf6fisgwev6i
Human Age Estimation With Regression on Discriminative Aging Manifold
2008
IEEE transactions on multimedia
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Through the manifold method of analysis on face images, the dimensionality redundancy of the original image space can be significantly reduced with subspace learning. ...
In this paper, we demonstrate that such aging patterns can be effectively extracted from a discriminant subspace learning algorithm and visualized as distinct manifold structures. ...
Each data point represents one face image. The data points of age from 0 to 93 are colored from blue to red. We choose four nearest neighbors for all the graph embedded learning algorithms. ...
doi:10.1109/tmm.2008.921847
fatcat:yhi6bh5wgfeybmn5wnbfh2nwee
Subspace clustering with dense representations
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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Unions of subspaces have recently been shown to provide a compact nonlinear signal model for collections of highdimensional data, such as large collections of images or videos. ...
In this paper, we introduce a novel data-driven algorithm for learning unions of subspaces directly from a collection of data; our approach is based upon forming minimum 2 -norm (least-squares) representations ...
Following this, we showcase the classification performance of DSC for segmenting images of faces under different illumination conditions. ...
doi:10.1109/icassp.2013.6638260
dblp:conf/icassp/DyerSB13
fatcat:phjkepjbwrglnowgae3tlod7om
Continuous Manifold Based Adaptation for Evolving Visual Domains
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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Our approach can learn to distinguish categories using training data collected at some point in the past, and continue to update its model of the categories for some time into the future, without receiving ...
Adaptation can be achieved via transforms or kernels learned between such stationary source and target subspaces. ...
This is not what the method was originally designed for, would be very computationally expensive and would require cross-validating or tuning a hyperparameter to choose the appropriate window size. ...
doi:10.1109/cvpr.2014.116
dblp:conf/cvpr/HoffmanDS14
fatcat:iw54yhxc3vgbnd5yh2caeyzvpq
Introductory Chapter: Face Recognition - Overview, Dimensionality Reduction, and Evaluation Methods
[chapter]
2016
Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods
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Other than this introductory chapter, this book has four more chapters, two chapters for dimensionality reduction and one for an overview of the face recognition systems and evaluation methods. ...
Over the past few decades, researchers in field of computers and electrical and electronics engineering have worked continuously to improve the performances of the face recognition systems. ...
This will help the students to choose an appropriate technique for doing their projects. Eight different potential applications of face recognition systems are highlighted in Chapter 1. ...
doi:10.5772/63995
fatcat:ojk4srxeyrf2nfxkxqvl64x22e
Classification of CASI-3 hyperspectral image by subspace method
2011
2011 IEEE International Geoscience and Remote Sensing Symposium
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Then, using the iterative learning technology of averaged learning subspace methods (ALSM) to rotate the subspaces slowly for optimizes the subspaces to get better classification accuracy. ...
This study presents a supervised subspace learning classification method which can be applied directly to the original set of spectral bands of hyperspectral data for land cover classification purpose. ...
ACKNOWLEDGMENT This work was supported by Grant-in-Aid for Scientific Research (B) (No. 21370005) from Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and supported by grants ...
doi:10.1109/igarss.2011.6049232
dblp:conf/igarss/HoshinoBNKKY11
fatcat:amwlsqojgbao3htocx3n67zozm
Active subspace learning
2009
2009 IEEE 12th International Conference on Computer Vision
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In this paper, we propose a novel active subspace learning algorithm which selects the most informative data points and uses them for learning an optimal subspace. ...
Experiments on image retrieval show improvement over state-of-the-art methods. ...
However, most of previous active learning approaches are designed for classification rather than subspace learning. ...
doi:10.1109/iccv.2009.5459329
dblp:conf/iccv/HeC09
fatcat:rt56w5gpw5g4vhrzfthayo3bye
Semi-supervised Node Splitting for Random Forest Construction
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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The proposed algorithm is compared with state-of-the-art supervised and semi-supervised algorithms for typical computer vision applications such as object categorization and image segmentation. ...
To avoid the curse of dimensionality, we project the data points from the original high-dimensional feature space onto a low-dimensional subspace before estimation. ...
Even the abundant data are unlabeled one can still choose the appropriate separating hyperplane as in (c) by combining the law of total probability and the kernel-based density estimation. ...
doi:10.1109/cvpr.2013.70
dblp:conf/cvpr/LiuSTLZCB13
fatcat:ywawii4ocredpml2cld6pcxtty
Deep Goal-Oriented Clustering
[article]
2020
arXiv
pre-print
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One could reasonably expect appropriately clustering the data would aid the downstream prediction task and, conversely, a better prediction performance for the downstream task could potentially inform ...
a more appropriate clustering strategy. ...
More concretely, if z ∼ p(z|c) for some index c, we assume p(y|z, c) ∝ g c (z) for some subspace-specific g c . As a result, we learn a different mapping function for each subspace. ...
arXiv:2006.04259v3
fatcat:hoorwgiohvh7db4aigkuw2e6b4
Neural Collaborative Subspace Clustering
[article]
2019
arXiv
pre-print
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We introduce the Neural Collaborative Subspace Clustering, a neural model that discovers clusters of data points drawn from a union of low-dimensional subspaces. ...
At its heart, our neural model benefits from a classifier which determines whether a pair of points lies on the same subspace or not. ...
In this paper, we formulate subspace clustering as a binary classification problem through collaborative learning of two modules, one for image classification and the other for subspace affinity learning ...
arXiv:1904.10596v1
fatcat:4bt3lvtnhje5faivs6wn5ngkcq
Learning Invariant Features Using Subspace Restricted Boltzmann Machine
2016
Neural Processing Letters
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We evaluate the behavior of subspaceRBM through experiments with MNIST digit recognition task and Caltech 101 Silhouettes image corpora, measuring cross-entropy reconstruction error and classification ...
The subspace units reflect variations of a pattern in data and the gate unit is responsible for activating the subspace units. Additionally, the gate unit can be seen as a pooling feature. ...
Introduction The success of machine learning methods stems from appropriate data representation. ...
doi:10.1007/s11063-016-9519-9
fatcat:nfk2nieopng4vaz42dyo6jju6q
Constrained Low-Rank Learning Using Least Squares-Based Regularization
2017
IEEE Transactions on Cybernetics
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2016 pre-print arXiv:1611.04870v1 fatcat:52qoz7mlkbcvnc6662iorkc6l4
Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. ...
Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their helpful and constructive comments that have greatly contributed to improving this manuscript. ...
doi:10.1109/tcyb.2016.2623638
pmid:27849552
fatcat:znrgzbzxu5g5pmdcyeb3rmgkgq
Clustering-based subspace SVM ensemble for relevance feedback learning
2008
2008 IEEE International Conference on Multimedia and Expo
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This paper presents a subspace SVM ensemble algorithm for adaptive relevance feedback (RF) learning. ...
Finally, regression results of multiple SVMs are probabilistic assembled to give the final labeling prediction for test image. ...
Tong [5] proposed a criterion to adaptively select the sampling points that closest to the classification hyper-plane for SVM training in relevance feedback. ...
doi:10.1109/icme.2008.4607661
dblp:conf/icmcs/JiYWXL08
fatcat:sy3pw3dwx5bszc6eyebbgn3elu
Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD
2014
Computerized Medical Imaging and Graphics
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To solve these challenges, we proposed a hybrid probabilistic sampling combined with diverse random subspace ensemble. ...
Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). ...
Acknowledgments This work is supported by the Alberta Innovates Centre for Machine Learning as well as the National Natural Science Foundation of China (61001047) and one author was supported by the China ...
doi:10.1016/j.compmedimag.2013.12.003
pmid:24418073
fatcat:r6bw75plkzdhvb2v6sikowrn2m
Domain adaptation for object recognition: An unsupervised approach
2011
2011 International Conference on Computer Vision
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Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on ...
the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. ...
From this we can compute the mean of source subspaces, sayS 1 , and the mean for targetS 2 . A popular method for defining the mean of points on a manifold was proposed by Karcher [24] . ...
doi:10.1109/iccv.2011.6126344
dblp:conf/iccv/GopalanLC11
fatcat:o2v5q7svefadhpf3lce5ox462a
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