The improved (2 D ) 2 PCA algorithm and its parallel implementation based on image block

Song, Haifeng; Chen, Guangsheng; Wei, Hairong; Yang, Weiwei

doi:10.1016/j.micpro.2016.06.011

Cited by 7 publications

(2 citation statements)

References 32 publications

(33 reference statements)

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“…Principal Component Analysis (PCA), a fundamental and widely employed technique, serves as a primary method for linear dimensionality reduction in hyperspectral image data. [12] . The core concept revolves around transforming an abundance of original variable indicators within the dataset into a limited set of principal components.…”

Section: Pcamentioning

confidence: 99%

Classification of wetland vegetation in the Yellow River Delta based on the fusion of hyperspectral data

Tian,

Qian,

Meng

2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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The Yellow River Delta wetlands are rich in vegetation species and the ecosystem is sensitive and fragile, so it is of great practical significance to accurately extract the information of the vegetation in the Yellow River Delta wetlands and analyze its spatial and temporal changes. Based on Sentinel-2A multispectral images and ZY1-02D hyperspectral images, this study addresses the problem of high spectral resolution and low spatial resolution of hyperspectral images, and adopts the Gram-Schmidt fusion method to fuse the two kinds of images, and uses the Random Forest Classification method to complete the classification of the typical vegetation of the Yellow River Delta region, and compares the classification accuracies of the three kinds of images. The study shows that: (1) Gram-Schmidt is more effective for image fusion, and the fusion of fused hyperspectral images based on the NIR band of Sentinel-2A images has the best fusion effect, which maximally retains the original spectral reflectance values, and at the same time improves the spatial resolution. (2) The classification accuracy of the fused hyperspectral images is high, with an overall classification accuracy of 85.62%, which is higher than that of single hyperspectral images and multispectral images.

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Section: Pcamentioning

confidence: 99%

Classification of wetland vegetation in the Yellow River Delta based on the fusion of hyperspectral data

Tian,

Qian,

Meng

2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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“…Then, these eigenspaces were merged to get projection matrix [32]. In one of the recent studies, a

false(2 normalD false) 2

PCA [33] method was improved and implemented on image block using MapReduce programming model for processing large‐scale images. Using this improved model, better scalability and speedup was obtained.…”

Section: Literature Summarymentioning

confidence: 99%

Class‐wise two‐dimensional PCA method for face recognition

Turhan

Bilge

2017

IET Computer Vision

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Interests in biometric identification systems have led to many face recognition task-oriented studies. These studies often address the detection of face images taken from a camera and the recognition of faces via extracted meaningful features. To meet the requirement of defining data with fewer features, principal component analysis (PCA)-based techniques are widely used due to their efficiency and simplicity. There is a remarkable interest in the used efficiency of PCA by extending this traditional technique with various aspects. From this viewpoint, this study is specifically focused on the PCA-based face recognition techniques. By enhancing the methods in the reviewed studies, a novel class-wise two-dimensional PCA-based face recognition algorithm is presented in this study. Unlike the traditional method, this method generates more than one subspace considering within-class scattering. A system based on the presented approach can successively detect and recognise faces in not only images but also in video files. In addition, analyses were conducted to evaluate the efficiency of the proposed algorithm and its extension comparing with other addressed PCA-based methods. On the basis of the experimental results, it is clear to say that the presented approach and its extension are superior to the compared PCA-based algorithms.

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A randomized generalized low rank approximations of matrices algorithm for high dimensionality reduction and image compression

2020

Numerical Linear Algebra App

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High-dimensionality reduction techniques are very important tools in machine learning and data mining. The method of generalized low rank approximations of matrices (GLRAM) is a popular technique for dimensionality reduction and image compression. However, it suffers from heavily computational overhead in practice, especially for data with high dimension. In order to reduce the cost of this algorithm, we propose a randomized GLRAM algorithm based on randomized singular value decomposition (RSVD). The theoretical contribution of our work is threefold. First, we discuss the decaying property of singular values of the matrices during iterations of the GLRAM algorithm, and provide a target rank required in the RSVD process from a theoretical point of view. Second, we establish the relationship between the reconstruction errors generated by the standard GLRAM algorithm and the randomized GLRAM algorithm. It is shown that the reconstruction errors generated by the former and the latter are comparable, even if the solutions are computed inaccurately during iterations. Third, the convergence of the randomized GLRAM algorithm is investigated. Numerical experiments on some real-world data sets illustrate the superiority of our proposed algorithm over its original counterpart and some state-of-the-art GLRAM-type algorithms. K E Y W O R D S generalized low rank approximations of matrices, high dimensionality reduction, image compression, randomized singular value decomposition, singular value decomposition

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The improved (2 D ) 2 PCA algorithm and its parallel implementation based on image block

Cited by 7 publications

References 32 publications

Classification of wetland vegetation in the Yellow River Delta based on the fusion of hyperspectral data

Classification of wetland vegetation in the Yellow River Delta based on the fusion of hyperspectral data

Class‐wise two‐dimensional PCA method for face recognition

A randomized generalized low rank approximations of matrices algorithm for high dimensionality reduction and image compression

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