Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

Sky image classification is crucial in meteorology to forecast weather and climatic conditions. The fine-grained cloud detection and recognition (FGCDR) algorithm is use to extract colour, inside texture and neighbour texture features from multiview of superpixels sky images. However, the FGCDR prod...

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Main Author:	Petwan, Montha
Format:	Thesis
Language:	eng eng
Published:	2023
Subjects:	Q Science (General)
Online Access:	https://etd.uum.edu.my/10897/1/permission%20to%20deposit-grant%20the%20permission-s902039.pdf https://etd.uum.edu.my/10897/2/s902039_01.pdf
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id	my-uum-etd.10897
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spelling	my-uum-etd.108972024-01-18T06:18:58Z Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification 2023 Petwan, Montha Ku Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Art & Sciences Q Science (General) Sky image classification is crucial in meteorology to forecast weather and climatic conditions. The fine-grained cloud detection and recognition (FGCDR) algorithm is use to extract colour, inside texture and neighbour texture features from multiview of superpixels sky images. However, the FGCDR produced a substantial amount of redundant and insignificant features. The ant colony optimisation (ACO) algorithm have been used to select feature subset. However, the ACO suffers from premature convergence which leads to poor feature subset. Therefore, an improved feature extraction and selection for sky image classification (FESSIC) algorithm is proposed. This algorithm consists of (i) Gaussian smoothness standard deviation method that formulates informative features within sky images; (ii) nearest-threshold based technique that converts feature map into a weighted directed graph to represent relationship between features; and (iii) an ant colony system with self-adaptive parameter technique for local pheromone update. The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. The Friedman test result is presented for the performance rank of six benchmark feature selection algorithms and FESSIC algorithm. The Man-Whitney U test is then performed to statistically evaluate the significance difference of the second rank and FESSIC algorithms. The experimental results for the proposed algorithm are superior to the benchmark image classification algorithms in terms of similarity value on Kiel, SWIMCAT and MGCD datasets. FESSIC outperforms other algorithms for average classification accuracy for the KSVM, MLP, RF and DT classifiers. The Friedman test has shown that the FESSIC has the first rank for all classifiers. Furthermore, the result of Man-Whitney U test indicates that FESSIC is significantly better than the second rank benchmark algorithm for all classifiers. In conclusion, the FESSIC can be utilised for image classification in various applications such as disaster management, medical diagnosis, industrial inspection, sports management, and content-based image retrieval. 2023 Thesis https://etd.uum.edu.my/10897/ https://etd.uum.edu.my/10897/1/permission%20to%20deposit-grant%20the%20permission-s902039.pdf text eng staffonly https://etd.uum.edu.my/10897/2/s902039_01.pdf text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku Mahamud, Ku Ruhana
topic	Q Science (General)
spellingShingle	Q Science (General) Petwan, Montha Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
description	Sky image classification is crucial in meteorology to forecast weather and climatic conditions. The fine-grained cloud detection and recognition (FGCDR) algorithm is use to extract colour, inside texture and neighbour texture features from multiview of superpixels sky images. However, the FGCDR produced a substantial amount of redundant and insignificant features. The ant colony optimisation (ACO) algorithm have been used to select feature subset. However, the ACO suffers from premature convergence which leads to poor feature subset. Therefore, an improved feature extraction and selection for sky image classification (FESSIC) algorithm is proposed. This algorithm consists of (i) Gaussian smoothness standard deviation method that formulates informative features within sky images; (ii) nearest-threshold based technique that converts feature map into a weighted directed graph to represent relationship between features; and (iii) an ant colony system with self-adaptive parameter technique for local pheromone update. The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. The Friedman test result is presented for the performance rank of six benchmark feature selection algorithms and FESSIC algorithm. The Man-Whitney U test is then performed to statistically evaluate the significance difference of the second rank and FESSIC algorithms. The experimental results for the proposed algorithm are superior to the benchmark image classification algorithms in terms of similarity value on Kiel, SWIMCAT and MGCD datasets. FESSIC outperforms other algorithms for average classification accuracy for the KSVM, MLP, RF and DT classifiers. The Friedman test has shown that the FESSIC has the first rank for all classifiers. Furthermore, the result of Man-Whitney U test indicates that FESSIC is significantly better than the second rank benchmark algorithm for all classifiers. In conclusion, the FESSIC can be utilised for image classification in various applications such as disaster management, medical diagnosis, industrial inspection, sports management, and content-based image retrieval.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Petwan, Montha
author_facet	Petwan, Montha
author_sort	Petwan, Montha
title	Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
title_short	Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
title_full	Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
title_fullStr	Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
title_full_unstemmed	Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
title_sort	feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2023
url	https://etd.uum.edu.my/10897/1/permission%20to%20deposit-grant%20the%20permission-s902039.pdf https://etd.uum.edu.my/10897/2/s902039_01.pdf
_version_	1794023784128184320

Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification

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