Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing Journal · Haziran 2015
YÖKSİS Kayıtları
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing · 2015 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing · 2015 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing · 2015 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models
2015 ISSN: 15684946 SCI-Expanded
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Color image segmentation based on multiobjective artificial bee colony optimization
2015 ISSN: 15684946 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Color image segmentation based on multiobjective artificial bee colony optimization
2015 ISSN: 15684946 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Liver fibrosis staging using CT image texture analysis and soft computing
2014 ISSN: 15684946 SCI-Expanded
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New Approaches to determine Age and Gender in Image Processing Techniques using Multilayer Perceptron Neural Network
2018 ISSN: 1568-4946 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
A modification of tree-seed algorithm using Deb’s rules for constrained optimization
2018 ISSN: 1568-4946 SCI Q1
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A hybrid binary grey wolf optimizer for selection and reduction of reference points with extreme learning machine approach on local GNSS/leveling geoid determination
2021 ISSN: 1568-4946 SCI-Expanded Q1
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Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
2021 ISSN: 1568-4946 SCI-Expanded Q1
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A discrete spotted hyena optimizer for solving distributed job shop scheduling problems
2021 ISSN: 1568-4946 SCI-Expanded Q1
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A hybrid binary grey wolf optimizer for selection and reduction of reference points with extreme learning machine approach on local GNSS/leveling geoid determination
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
Classification rule mining based on Pareto-based Multiobjective Optimization
2022 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. TAHİR SAĞ →
Classification rule mining based on Pareto-based Multiobjective Optimization
2022 ISSN: 1568-4946 SCI-Expanded Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
Image forgery detection by combining Visual Transformer with Variational Autoencoder Network
2024 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. ALİ YAŞAR →
Parametric picture fuzzy cross-entropy measures based on d-Choquet integral for building material recognition Applied Soft Computing
2024 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. DİLEK SÖYLEMEZ ÖZDEN →
A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling
2025 ISSN: 1568-4946 SCI-Expanded
Doç. Dr. AHMET CEVAHİR ÇINAR →
Efficiency analysis of binary metaheuristic optimization algorithms for uncapacitated facility location problems
2025 ISSN: 1568-4946 SCI-Expanded Q1
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Makale Bilgileri
ISSN15684946
Yayın TarihiHaziran 2015
Cilt / Sayfa34 · 389-401
Scopus ID2-s2.0-84930946635
Özet
This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.
Yazarlar (2)
1
Tahir Saǧ
2
Mehmet Çunkaş
Anahtar Kelimeler
Artificial bee colony
Color image segmentation
Fuzzy c-means
Multiobjective optimization
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Applied Soft Computing
Q1
SJR Skoru1,511
H-Index208
YayıncıElsevier B.V.
ÜlkeNetherlands
Software (Q1)
Metrikler
77
Atıf
2
Yazar
4
Anahtar Kelime