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Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification
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- Saeed, Ayesha (author)
- Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan.
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- Khan, Muhammad Jamil (author)
- Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan.
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- Riaz, Muhammad Ali (author)
- Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan.
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- Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan COMSATS Univ Islamabad, Math Dept, Islamabad 45550, Pakistan. (creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
- 2019
- English.
- In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 110116-110127
- Journal article (peer-reviewed)
Abstract
Subject headings
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- A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Feature descriptor
- texture classification
- Gaussian derivatives
- wavelet decomposition
- local binary pattern
- noise robust
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Loo, Jonathan
- Tenhunen, Hannu
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- ENGINEERING AND TECHNOLOGY
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- IEEE Access
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- Royal Institute of Technology
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