A critique of SVD-based image coding systems

Aase, S.O.; Husøy, J.H.; Waldemar, P.

doi:10.1109/iscas.1999.779931

Cited by 30 publications

(12 citation statements)

References 6 publications

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“…2. However we can notice that most energy information is confined within the small first number of singular values [17]. Therefore, the amount of redundant data can be reduced using the truncation method to achieve a better computational efficiency.…”

Section: Human Body Feature Extractionmentioning

confidence: 99%

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Automatic Classification of Human Body Postures Based on the Truncated SVD

Zerrouki¹,

Houacine²

2014

JACN

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Abstract-In this experimental study, we propose the use of Singular Value Decomposition (SVD) coefficients as features to automatically classify human body postures. The classification process uses images extracted from a fixed camera video. A background subtraction technique is applied for human body segmentation. A truncated SVD is performed by selecting significant magnitude coefficients. And the height-width ratio of the human body is also included in the set of features. The classification is then performed using an Artificial Neural Network (ANN). Four body postures are considered in our experiments, namely: standing, bending, sitting, and lying. Evaluation results show that the proposed method achieved 90.46% classification accuracy. Truncated SVD coefficients and height-width ratio as body posture features are thus appropriate descriptors to achieve high classification accuracy. Also, the proposed method yields the best classification accuracy compared to well-known classification methods. Index Terms-Human body postures, classification, SVD coefficients, neural network. I. INTRODUCTIONHuman behavior understanding remains an interesting study area in intelligent video monitoring because it is closely related to computer vision applications and automatic surveillance. It is a very challenging and complex task due to some difficulties such as variations in people's appearance, skin color, illumination conditions, and the amount of data generated. To reduce the complexity, many previous works have focused on some specific topics such as face recognition [1], body tracking [2], and hand gesture recognition [3] from a single image or multiple images.Behavior understanding includes mainly motion pattern identification or body posture analysis and classification. Many approaches have been proposed for human posture classification. Poppe et al. [4] use a degree of freedom to estimate the body posture for a presenter in meeting environments. Hooi et al. propose a computer vision-based approach to automatically detect human body parts and estimate the human body postures from a monocular video sequence [5].However, most of the stated methods use skin color to locate hands and head. The person is also supposed to face the camera which can be considered as a limitation.Capo et al. present a computer vision algorithm to automatically model the human body skeleton using Bayesian networks classifier [6]. In [7] the body is decomposed into its Manuscript received August 25, 2013; revised October 29, 2013. The authors are with LCPTS laboratory, University of Sciences and Technology Houari Boumé dienne, Algeria (e-mail: nzerrouki@usthb.dz, ahouacine@usthb.dz). main parts, it constructs a silhouette based body model to determine the location of the body parts [8]. But recognition of human parts is not an easy task as it requires processing based on only visible parts of the body. Lutz Goldmann et al.[9] use appearance-based features for posture description such as color, shape, texture, motion, and size. Fast Fourier Tran...

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Section: Human Body Feature Extractionmentioning

confidence: 99%

“…The essential concept of the truncated SVD is to use only a small number of singular values to approximate the original image [17]. Given m by n image I, SVD decomposition consists of the factorization of the image I into three matrices as follows:…”

Section: Human Body Feature Extractionmentioning

confidence: 99%

Automatic Classification of Human Body Postures Based on the Truncated SVD

Zerrouki¹,

Houacine²

2014

JACN

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“…In recent years, SVD has been used in watermarking as a different transform as it is one of the most powerful tools of linear algebra with several applications in image compression [8,9,10,11,12,13], watermarking [14,15,16,17]. Singular values are the luminance values of SVD image layer, changing these values slightly do not affect the image quality much [18].The purpose of singular value decomposition is to reduce a dataset containing a large number of values to a dataset containing significantly fewer values, but which still contains a large fraction of the variability present in the original data.…”

Section: Lifting Wavelet Transform(lwt) and Singular Value Transfmentioning

confidence: 99%

A hybrid semi-blind digital image watermarking technique using lifting wavelet transform — Singular value decomposition

Arya

Siddavatam

Ghrera

2011

2011 Ieee International Conference on Electro/Information Technology

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The lifting wavelet transform (LWT) is a recent approach to wavelet transform and singular value decomposition (SVD) is a valuable transform technique for robust digital watermarking. While LWT allows generating an infinite number of discrete biorthogonal wavelets starting from an initial one, singular values (SV) allow us to make changes in an image without affecting the image quality much. This paper presents an approach which tries to amalgamate the features of these two transforms to achieve a hybrid and robust digital image watermarking techniques. Certain performance metrics are used to test the robustness of the method against common image processing attacks.

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“…It is done by embedding additional information called digital signature or watermark into the digital contents such that it can be detected, extracted later to make an assertion about the multimedia data. [1,2] For image cryptography, the algorithms can be categorized into one of the two domains: spatial domain or transform domain. [1,2] In Spatial domain the data is embedded directly by modifying pixel values of the host image, while transform domain schemes embed data by modifying transform domain coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…[1,2] For image cryptography, the algorithms can be categorized into one of the two domains: spatial domain or transform domain. [1,2] In Spatial domain the data is embedded directly by modifying pixel values of the host image, while transform domain schemes embed data by modifying transform domain coefficients. Algorithms used for special domain are less robust for various attacks as the changes are made at least Significant Substitution (LSB) of original data.…”

Section: Introductionmentioning

confidence: 99%

SVD-DWT Amalgamated Cryptography Scheme Antagonistic to Noise Attacks

Dwivedi¹,

Saxena²

2018

IJCA

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In order to improve the robustness and imperceptibleness of the algorithm, a new embedding and extracting method with DWT-SVD is proposed. The approximation matrix of the third level of image in DWT domain is modified with SVD to embed the singular value of watermark to the singular value of DWT coefficient. The proposed embedding and extracting method was employed to accelerate the hybrid DWT-SVD cryptography and to avoid the leak of watermark. This hybrid technique leads to optimize both the fundamentally conflicting requirements. The experimental results show both the good robustness under numerous attacks and the high fidelity. The time needed to perform the program is greatly decreased.

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A critique of SVD-based image coding systems

Cited by 30 publications

References 6 publications

Automatic Classification of Human Body Postures Based on the Truncated SVD

Automatic Classification of Human Body Postures Based on the Truncated SVD

A hybrid semi-blind digital image watermarking technique using lifting wavelet transform — Singular value decomposition

SVD-DWT Amalgamated Cryptography Scheme Antagonistic to Noise Attacks

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A critique of SVD-based image coding systems

Cited by 30 publications

References 6 publications

Automatic Classification of Human Body Postures Based on the Truncated SVD

Automatic Classification of Human Body Postures Based on the Truncated SVD

A hybrid semi-blind digital image watermarking technique using lifting wavelet transform &#x2014; Singular value decomposition

SVD-DWT Amalgamated Cryptography Scheme Antagonistic to Noise Attacks

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A hybrid semi-blind digital image watermarking technique using lifting wavelet transform — Singular value decomposition