QWT enhanced SVM for Hyperspectral image classification

Shen, Yue; Feng, Hongqi; Wang, Qiang; Liu, Yipeng; He, Zhi

doi:10.1109/i2mtc.2014.6860986

The development of the quaternion wavelet transform

Fletcher

¹

,

Sangwine

²

2017

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The development of the quaternion wavelet transform, Signal Processing, http://dx.doi.org/10. 1016/j.sigpro.2016.12.025 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. we then go on to detail published approaches to QWTs and to analyse them. We conclude with our own analysis of what it is that should define a QWT as being truly quaternionic and why all but a few of the "QWTs" we have described do not fit our definition.

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Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Wang¹,

Lu²,

Xiang³

et al. 2020

Digital Forensics and Forensic Investigations

8

0

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Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

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Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Wang

¹

,

Lu

²

,

Li

³

et al. 2018

International Journal of Digital Crime and Forensics

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Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

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QWT enhanced SVM for Hyperspectral image classification

Cited by 3 publications

References 8 publications

The development of the quaternion wavelet transform

The development of the quaternion wavelet transform

Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

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