Hyperspectral image, video compression using sparse tucker tensor decomposition

Das, Samiran

doi:10.1049/ipr2.12077

Cited by 25 publications

(18 citation statements)

References 41 publications

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“…Based on them, many tensor-based dimensionality reduction algorithms are developed from famous vector-based dimensionality reduction, such as multilinear PCA [20], tensor marginal fisher analysis [14], tensor neighbourhood preserving embedding [13], tensor locality preserving projection [13], and so on. In recent years, some state-of-the-art tensor decomposition models or TDR algorithms [15,16,24,30,33,39,40] are proposed. Here, some related TDR work are described in detail, especially which are used in the following experiments for comparison.…”

Section: Related Workmentioning

confidence: 99%

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Tensor local linear embedding with global subspace projection optimisation

Niu

2021

IET Computer Vision

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In this paper, a novel tensor dimensionality reduction (TDR) approach is proposed, which maintains the local geometric structure of tensor data by tensor local linear embedding and explores the global feature by optimising global subspace projection. Firstly, we analyse the local linear feature of tensor data for learning the linear separable embedding of the tenor data. Furthermore, a global subspace projection distance minimisation strategy is introduced to extract the global characteristic of the tensor data. The aim of this strategy is to find an optimal low-dimensional subspace for TDR. In particular, two novel TDR algorithms are developed by the ensemble of tensor local feature preservation and global subspace projection distance minimisation, which express the subspace projection optimisation as an iteration optimisation problem and a Rayleigh quotient problem, respectively. The extensive experimental results on tensor data classification and clustering have demonstrated the proposed algorithms performed well.

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Section: Related Workmentioning

confidence: 99%

“…Tensor data represents multi-dimensional data, in which each dimension involves the inherent structure information of the original data. The TDR methods learn the low-dimensional representation of tensor data with various strategies [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Tensor local linear embedding with global subspace projection optimisation

Niu

2021

IET Computer Vision

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“…Of note, in Bayesian frameworks and NMF-based approaches, the 3D data structure is stacked into a matrix form, which causes a loss in the neighborhood structures, smoothness, and continuity characteristics. To avoid this, tensors or multiway arrays have been frequently used in hyperspectral data analysis for the purposes of image classification [31][32][33], data compression [34,35], change detection [36,37], target and anomaly detection [36,38], and denoising [39,40]. Additionally, exploiting the capability of multilinear algebra on the multiway array representations allows more flexibility in choosing constraints and describing data structures.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

et al. 2021

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Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.

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“…Some other researchers focus on tensor decomposition for TDR. Such as Zhou et al [27] proposed an effective non-negative Tucker decomposition (NTD) to approximate the tensor by the mode product of a low-dimensional tensor with effective projection matrices; Zhang et al [28] used the low-rank regularized heterogeneous tensor decomposition (LRR-HTD) to learn a set of orthogonal factor matrices for data; Cai et al [29] proposed a regularized non-negative matrix factorization (GNMF) algorithm, which introduced an affinity graph to obtain the geometric information for seeking a factor matrix factorization; [30] applied sparse Tucker tensor decomposition to the compression of the hyperspectral image or video sequence.…”

Section: Introductionmentioning

confidence: 99%

“…[28] used the low‐rank regularized heterogeneous tensor decomposition (LRR‐HTD) to learn a set of orthogonal factor matrices for data; Cai et al. [29] proposed a regularized non‐negative matrix factorization (GNMF) algorithm, which introduced an affinity graph to obtain the geometric information for seeking a factor matrix factorization; [30] applied sparse Tucker tensor decomposition to the compression of the hyperspectral image or video sequence.…”

Section: Introductionmentioning

confidence: 99%

Tensor dimensionality reduction via mode product and HSIC

Niu

2021

IET Image Processing

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Tensor dimensionality reduction (TDR) is a hot research topic in machine learning, which learns data representations by preserving the original data structure while avoiding convert samples into vectors and solving the problem of the curse of dimensionality of tensor data. In the work, a novel TDR approach based on mode product and Hilbert-Schmidt Independence criterion (HSIC) is proposed. The contributions of authors' work is described as following: (1) HSIC measures the statistical correlation of two random variables. However, instead of measuring the statistical correlation of two random variables directly, HSIC first transforms the two random variables into two reproducing kernel Hilbert spaces (RKHSs), and then measures the statistical correlation of transformed random variables by using Hilbert-Schmidt operators between the two RKHSs. The exploitation of RKHS increases the flexibility and applicability of HSIC. Although HSIC is widely used in machine learning, the authors have not seen its application to dimensionality reduction (DR)(except for authors' previous work). (2) A novel HSIC-based TDR approach is proposed, which first applies HSIC to capture statistical information of tensor data set for DR. The authors give the mathematical derivation of HSIC for tensor data and establish a framework of TDR based on HSIC, named HSIC-TDR for short, which aims to improve the DR results of tensor by exploring and preserving the statistical information of original data set. (3) Furthermore, to solve the out-of-sample problem, the authors learn an explicit expression between the dimensionality-reduced tensors and the higher-dimensional tensors by introducing mode product to HSIC-TDR. The experimental results between the proposed method and other state-of-the-art algorithm on various datasets demonstrate the well performance of the proposed method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Hyperspectral image, video compression using sparse tucker tensor decomposition

Cited by 25 publications

References 41 publications

Tensor local linear embedding with global subspace projection optimisation

Tensor local linear embedding with global subspace projection optimisation

Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

Tensor dimensionality reduction via mode product and HSIC

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