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Color image classification via quaternion principal component analysis network

Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Beijing Chen, Lotfi Senhadji, Huazhong Shu
2016 Neurocomputing  
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]

Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Lotfi Senhadji, Huazhong Shu
2015 arXiv   pre-print
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

Meixiang Zhao, Zhigang Jia, Dunwei Gong
2019 IEEE Access  
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

Uzair Aslam Bhatti, Zhaoyuan Yu, Linwang Yuan, Saqib Ali Nawaz, Mughair Bhatti, Anum Mehmood, Qurat Ul Ain, Z. Zeehsan, Luo Wen
2020 IEEE Access  
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

Eckhard Hitzer, Tohru Nitta, Yasuaki Kuroe
2013 Advances in Applied Clifford Algebras  
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]

Yigit Oktar, Mehmet Turkan
2020 arXiv   pre-print
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

Long H. Ngo, Nikolay M. Sirakov, Marie Luong, Emmanuel Viennet, Thuong Le-Tien
2022 IEEE Access  
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

Beijing Chen, Huazhong Shu, Gouenou Coatrieux, Gang Chen, Xingming Sun, Jean Louis Coatrieux
2014 Journal of Mathematical Imaging and Vision  
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]

Julien Flamant, Sebastian Miron, David Brie
2021 arXiv   pre-print
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  
., +, 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]

Aurora Sáez, Begoña Acha, Carmen Serrano
2013 Series in BioEngineering  
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

Saiful Islam, Mohammad Farhad Bulbul, Md. Sirajul Islam
2018 Machine Learning and Applications An International Journal  
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

Jie Wang, Chang Luo, Hanqiao Huang, Huizhen Zhao, Shiqiang Wang
2017 Remote Sensing  
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  
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

Ali Maina Bukar, Hassan Ugail, David Connah
2016 Journal of Electronic Imaging (JEI)  
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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