Scopus
YÖKSİS Eşleşti
Automated classification of hand-woven and machine-woven carpets based on morphological features using machine learning algorithms
Journal of the Textile Institute · Ocak 2024
YÖKSİS Kayıtları
Automated Classification of Hand-Woven and Machine-Woven Carpets based on Morphological Features Using Machine Learning Algorithms
The Journal of The Textile Institute · 2024 SCI-Expanded
DOÇENT YAVUZ SELİM TAŞPINAR →
Automated Classification of Hand-Woven and Machine-Woven Carpets based on Morphological Features Using Machine Learning Algorithms
The Journal of The Textile Institute · 2024 SCI-Expanded
DOÇENT MURAT KÖKLÜ →
Automated Classification of Hand-Woven and Machine-Woven Carpets based on Morphological Features Using Machine Learning Algorithms
The Journal of The Textile Institute · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ İLKAY ÇINAR →
Automated Classification of Hand-Woven and Machine-Woven Carpets based on Morphological Features Using Machine Learning Algorithms
The Journal of The Textile Institute · 2024 SCI-Expanded
ÖĞRETİM GÖREVLİSİ RAMAZAN KURŞUN →
Makale Bilgileri
DergiJournal of the Textile Institute
Yayın TarihiOcak 2024
Scopus ID2-s2.0-85185333215
Özet
As a cultural heritage, hand-woven carpets engender trust and admiration in individuals who recognize their authenticity. It is the expertise of experts who determine whether a carpet is hand-woven or machine-woven based on authenticity criteria. A total of 48 morphological features were extracted by three carpet experts from 359 handwoven and machine woven carpets. Machine-learning algorithms were used to classify the extracted features. With an accuracy of 96.66%, the ANN algorithm achieved the best classification performance. Afterward, 28 morphological features were selected with the highest gain ratios and reclassified. Based on the selected features, SVM (Support Vector Machine) achieved the best classification accuracy of 96.66%. A carpet expert performed classification using the morphological features extracted by machine learning algorithms to evaluate the classification results obtained through machine learning algorithms. When 48 morphological features were used, the accuracy was 98.61%, and when 28 morphological features were used, the accuracy was 97.77%. Based on the results, artificial intelligence techniques are suitable for detecting morphological features in hand-woven and machine-woven carpets and for automatically classifying them.
Yazarlar (7)
1
Melike Isik
2
Burcu Ozulku
3
Ramazan Kursun
ORCID: 0000-0002-6729-1055
4
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
5
Ilkay Cinar
ORCID: 0000-0003-0611-3316
6
Elham Tahsin Yasin
ORCID: 0000-0003-3246-6000
7
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
carpet classification
Hand-woven carpet
machine learning algorithms
machine-woven carpet
morphological features of carpets
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
1
Atıf
7
Yazar
5
Anahtar Kelime