Deep invariant texture features for water image classification

Xue, M; Shivakumara, P; Wu, X; Lu, T; Pal, U; Blumenstein, M; Lopresti, D

Deep invariant texture features for water image classification

Xue, M Shivakumara, P Wu, X Lu, T Pal, U Blumenstein, M

Lopresti, D

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: SN Applied Sciences, 2020, 2, (12), pp. 2068
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Xue, M
dc.contributor.author	Shivakumara, P
dc.contributor.author	Wu, X
dc.contributor.author	Lu, T
dc.contributor.author	Pal, U
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.contributor.author	Lopresti, D
dc.date.accessioned	2021-05-24T23:54:23Z
dc.date.available	2021-05-24T23:54:23Z
dc.date.issued	2020-12-01
dc.identifier.citation	SN Applied Sciences, 2020, 2, (12), pp. 2068
dc.identifier.issn	2523-3971
dc.identifier.issn	2523-3971
dc.identifier.uri	http://hdl.handle.net/10453/149182
dc.description.abstract	Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	SN Applied Sciences
dc.relation.isbasedon	10.1007/s42452-020-03882-w
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The final publication is available at Springer via http://dx.doi.org/ 10.1007/s42452-020-03882-w
dc.title	Deep invariant texture features for water image classification
dc.type	Journal Article
utslib.citation.volume	2
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-05-24T23:54:19Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	12

Abstract:

Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.

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http://hdl.handle.net/10453/149182