Scopus
YÖKSİS Eşleşti
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
DOÇENT TAHİR SAĞ →
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing · 2015 SCI-Expanded
PROFESÖR MEHMET ÇUNKAŞ →
Color image segmentation based on multiobjective artificial bee colony optimization
Applied Soft Computing · 2015 SCI-Expanded
PROFESÖR MEHMET ÇUNKAŞ →
Makale Bilgileri
DergiApplied Soft Computing Journal
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
Metrikler
77
Atıf
2
Yazar
4
Anahtar Kelime