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Color image classification via quaternion principal component analysis network
2016
Neurocomputing
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Color image classification via quaternion principal component analysis network. Neurocomputing, Elsevier, 2016, 216, pp. ...
In this paper, a quaternion principal component analysis network (QPCANet), which extends PCANet by using quaternion theory, is proposed for color image classification. ...
In this paper, to address the above issues, we propose a new color image feature extraction algorithm, namely, quaternion principal component analysis network (QPCANet) which is a quaternion deep learning ...
doi:10.1016/j.neucom.2016.08.006
fatcat:goizytab6zhntilbyy5vx7bglq
Color Image Classification via Quaternion Principal Component Analysis Network
[article]
2015
arXiv
pre-print
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In this paper, a Quaternion Principal Component Analysis Network (QPCANet), which is an extension of PCANet, is proposed for color images classification. ...
The Principal Component Analysis Network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases. ...
In this paper, we propose a new color image feature extraction technique, namely Quaternion Principal Component Analysis Network (QPCANet) that extends and adapts the principals of PCANet approach to quaternions ...
arXiv:1503.01657v1
fatcat:qjhjeqwdbfh4royazd6mukdlq4
Improved Two-Dimensional Quaternion Principal Component Analysis
2019
IEEE Access
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The two-dimensional quaternion principal component analysis (2D-QPCA) is first improved into abstracting the features of quaternion matrix samples in both row and column directions, being the generalization ...
INDEX TERMS 2D-QPCA, quaternion matrix, generalization ability, color face recognition, image reconstruction, eigenvalue problem. ...
Shu, ''Color image classification via quaternion principal component analysis network,'' Neurocomputing, vol. 216, pp. 416-428, Dec. 2016. [46] Z. Zhang, M. Zhao, B. Li, P. Tang, and F.-Z. ...
doi:10.1109/access.2019.2923359
fatcat:44mkg6aoevb4jbc3d2dwgaidaa
Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing
2020
IEEE Access
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Principal component regression (PCR) is useful for extracting features of hyperplanes [47] . ...
In our other work [1] , we used the quaternion Fourier transform (QFT) to watermark color images as RGB components are perfectly handled in quaternion-based CA. Hsu et al. ...
He is currently doing masters from Hainan University, His major research interests include intelligent data analysis and data mining, ultrawideband communications, wireless communications, sensor networks ...
doi:10.1109/access.2020.3018544
fatcat:dxnehrysjbh3fmsbmolkqnzfbu
Applications of Clifford's Geometric Algebra
2013
Advances in Applied Clifford Algebras
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solutions in a unified framework for algebra and geometry with the widest possible scope: from quantum computing and electromagnetism to satellite navigation, from neural computing to camera geometry, image ...
A number of principal experiments with test images, some including color, is performed and the various quaternionic components and phases are discussed. ...
Color image processing Conventional color image processing relies on marginal channel wise red, green and blue color processing. ...
doi:10.1007/s00006-013-0378-4
fatcat:xmnxscftojhnpgxtyrwrqnjic4
On the Preservation of Spatio-temporal Information in Machine Learning Applications
[article]
2020
arXiv
pre-print
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In this paper, the problem of orthogonality is first investigated through conventional k-means of images, where images are to be processed as vectors. ...
A generalization of shift-invariant k-means, convolutional dictionary learning, is then utilized as an unsupervised feature extraction method for classification. ...
The last two methods to be mentioned, namely Autoencoders (AE) and Principal Component Analysis (PCA) both perform dimensionality reduction. ...
arXiv:2006.08321v1
fatcat:fokwd5vtvrdipkyc2tmavaivju
Image Classification Based on Sparse Representation in the Quaternion Wavelet Domain
2022
IEEE Access
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meaningful features from low-frequency QW subbands, ii) the dimensionality reduction method using Principal Component Analysis (PCA) for reducing the complexity of the problem, and iii) the sparse representation ...
In this study, we propose a novel sparse representation learning method in the Quaternion Wavelet (QW) domain for multi-class image classification. ...
In our new method, training and test samples are transformed into the QW domain and principal component analysis (PCA) is used to reduce the dimension of the generated features and computational cost. ...
doi:10.1109/access.2022.3159701
fatcat:bxv6slkpobaq5ngxrizajj47qu
Color Image Analysis by Quaternion-Type Moments
2014
Journal of Mathematical Imaging and Vision
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They include the Fourier transform [3, 9, 17, 18] , neural networks [19, 20], principal component analysis [8, 14], the wavelet transform [21, 22], independent component analysis [23, 24], singular value ...
In the past two decades, quaternions have been more and more used in color image processing to represent color images by encoding three channels into the imaginary parts of quaternion numbers [1, 3-16] ...
analysis (QBDPCA), which offers the best performance when compared to other quaternion-based principal component analysis according to [14] . ...
doi:10.1007/s10851-014-0511-6
fatcat:fv5ythriovfrlds4tf4mxuajeq
A general framework for constrained convex quaternion optimization
[article]
2021
arXiv
pre-print
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Our results open new avenues in the design, analysis and efficient implementation of quaternion-domain optimization procedures. ...
This paper introduces a general framework for solving constrained convex quaternion optimization problems in the quaternion domain. ...
Sangwine, “Quaternion principal component
analysis of color images,” in Proceedings 2003 International Con-
ference on Image Processing (Cat. No. 03CH37429), IEEE, vol. 1,
2003, pp. ...
arXiv:2102.02763v2
fatcat:hcx7anzmxjayzmll7hu6pasmou
2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25
2014
IEEE Transactions on Neural Networks and Learning Systems
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., +, TNNLS Apr. 2014
793-805
Multilinear Sparse Principal Component Analysis. ...
Pang, Y., +, TNNLS Dec. 2014
2191-2201
Multilinear Sparse Principal Component Analysis. ...
The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014 ...
doi:10.1109/tnnls.2015.2396731
fatcat:ztnfcozrejhhfdwg7t2f5xlype
Pattern Analysis in Dermoscopic Images
[chapter]
2013
Series in BioEngineering
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In this chapter, a description of global and local patterns identified by pattern analysis is presented as well as a brief explanation of algorithmic methods that carry out the detection and classification ...
In this chapter an extensive review of algorithmic methods that automatically detect patterns in dermoscopic images of pigmented lesions is presented. ...
The color image is converted via principal component analysis (PCA) and a two dimensional (2-D) histogram is computed with the two first principal components. ...
doi:10.1007/978-3-642-39608-3_2
fatcat:6pepa6zagzhexges7hkj6zvkra
A Comparative Study on Human Action Recognition Using Multiple Skeletal Features and Multiclass Support Vector Machine
2018
Machine Learning and Applications An International Journal
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HAR has become a basis for applications such as health care, fall detection, human position tracking, video analysis, security applications, etc. ...
Wehave used joint angle quaternion and absolute joint position to recognitionhuman action. We also mapped joint position on ( 3) Lie algebra and fuse it with other features. ...
[22] developed a method of principal geodesic analysis, a generalization of principal component analysis to the manifold setting. ...
doi:10.5121/mlaij.2018.5201
fatcat:v72ltzvgx5gwnca6xcrpqykqfy
Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network
2017
Remote Sensing
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Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral "distance" between remote sensing images and images used to pre-train deep CNNs. ...
First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. ...
PCA network (PCANet) is a simple but effective neural network, which mainly comprises three components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms [27] . ...
doi:10.3390/rs9030225
fatcat:ghpzuij5uzavhh7aaxbqfkxlra
Table of contents
2020
IEEE Transactions on Image Processing
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Pavlovic 3993 Sensing Matrix Design for Compressive Spectral Imaging via Binary Principal Component Analysis .................. ......................................................................... ...
Hu 1413 Low-Rank Quaternion Approximation for Color Image Processing ...................... Y. Chen, X. Xiao, and Y. ...
doi:10.1109/tip.2019.2940372
fatcat:h23ul2rqazbstcho46uv3lunku
Automatic age and gender classification using supervised appearance model
2016
Journal of Electronic Imaging (JEI)
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This is primarily because principal component analysis (PCA), which is at the core of the model, works in an unsupervised manner, i.e., PCA dimensionality reduction does not take into account how the predictor ...
However, AAM suffers from some drawbacks, especially when used for classification. ...
This process is driven by principal component analysis (PCA), a dimensionality reduction technique, which is also used to combine the texture and shape vectors. ...
doi:10.1117/1.jei.25.6.061605
fatcat:gnxbdgoeyrab7bg5apx7pipvwu
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