Two-dimensional principal component analysis based on Schatten p -norm for image feature extraction

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA). Two-dimensional principal component analysis (2DPCA) is widely used in face recognition. We further study on the basis of 2DPCA. This proposed method is to add a parameter to images samples matrix in the image covariance matrix. Extensive experiments are performed on FERET face database and CMU PIE face database. The 2DPPCA method achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.

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Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

Journal of Sensors ◽

10.1155/2019/5736104 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Tsun-Kuo Lin

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

High Performance ◽

Principal Component ◽

Component Analysis ◽

Image Features ◽

Image Feature ◽

Support Vector ◽

Feature Identification ◽

Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

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Matrix Based Feature Measurement and Extraction for Face Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.738-739.643 ◽

2015 ◽

Vol 738-739 ◽

pp. 643-647

Author(s):

Qi Zhu ◽

Jin Rong Cui ◽

Zi Zhu Fan

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Face Recognition ◽

Measurement Method ◽

Principal Component ◽

Component Analysis ◽

High Accuracy ◽

Face Image ◽

Two Dimensional ◽

The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

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An Incremental Two-Dimensional Principal Component Analysis for Object Recognition

Mathematical Problems in Engineering ◽

10.1155/2018/3272809 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Weimin Ge ◽

Mingyue Sun ◽

Xiaofeng Wang

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Object Recognition ◽

Convergence Rates ◽

Principal Component ◽

Component Analysis ◽

Video Stream ◽

Two Dimensional ◽

Batch Mode ◽

Time Requirements

Two-dimensional principal component analysis algorithm (2DPCA) can be performed in the batch mode and can not meet the real-time requirements of the video stream. To overcome these limitations, the incremental learning of the candid covariance-free incremental PCA (CCIPCA) is innovated to the existing 2DPCA, and the called incremental 2DPCA (I2DPCA) is firstly presented to incrementally compute the principal components of a sequence of samples directly on the 2D image matrices without estimating the covariance matrices. Therefore, the I2DPCA can improve the feature extraction speed and reduce the required memory. However, the variations between the column direction, generally neglected, are also useful for the high-accuracy object recognition. Thus, another incremental sequential row-column 2DPCA algorithm (IRC2DPCA), based on the proposed I2DPCA algorithm, is also proposed. The IRC2DPCA can compress the image matrices in the row and column direction, and the feature matrices extracted by the IRC2DPCA are with less dimensions than the I2DPCA. The substantial experimental results show that the IRC2DPCA, compared with the other three algorithms, can improve the convergence rates and the recognition rates, compress the dimensions of the feature matrices, and reduce the feature extraction time and the classification time.

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Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217704035 ◽

2017 ◽

Vol 40 (7) ◽

pp. 2387-2395 ◽

Cited By ~ 1

Author(s):

Yi Ji ◽

Hong-Bo Xie

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Computational Cost ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Time Frequency ◽

Frequency Decomposition ◽

S Transform ◽

Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

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