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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:English
English
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
https://etd.uum.edu.my/10897/
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spelling my.uum.etd.108972024-01-18T06:18:58Z https://etd.uum.edu.my/10897/ Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification Petwan, Montha 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 NonPeerReviewed text en https://etd.uum.edu.my/10897/1/permission%20to%20deposit-grant%20the%20permission-s902039.pdf text en https://etd.uum.edu.my/10897/2/s902039_01.pdf Petwan, Montha (2023) Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification. Doctoral thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
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
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
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
https://etd.uum.edu.my/10897/
_version_ 1789428106170204160
score 13.211869

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