Meta sezgisel arama algoritmalarının çok seviyeli görüntü eşikleme performanslarının karşılaştırılması

Year 2025, Volume: 14 Issue: 1, 1 - 1

Asuman Günay Yılmaz , Samoua Alsamoua

https://doi.org/10.28948/ngumuh.1529614

Abstract

Görüntü segmentasyonu alanında sıklıkla kullanılan eşiklemeye dayalı segmentasyon yöntemleri, az sayıdaki eşik değerinin belirlenmesinde başarılıdır. Fakat eşik seviye sayısı arttıkça hesaplama karmaşıklığında üstel artış meydana gelmektedir. Bu nedenle problemin çözümünde meta sezgisel arama algoritmaları tercih edilmektedir. Bu çalışmada görüntü segmentasyonu literatüründe en çok kullanılan 11 meta sezgisel algoritmanın (yapay arı kolonisi, guguk kuşu arama, diferansiyel evrim, gri kurt optimizasyonu, harris şahini optimizasyonu, güve-alev optimizasyonu, parçacık sürüsü optimizasyonu, sinüs kosinüs algoritması, simbiyotik organizmalar arama, salp sürü algoritması, balina optimizasyon algoritması) performansı kıyaslanmıştır. m=2,3,4,5,6,7,8 seviyeli segmentasyon ile algoritmaların düşük ve orta seviyelerdeki başarımı değerlendirilmiştir. 100 görüntü üzerinde yapılan deney sonuçları Friedman istatistiksel analiz yöntemi ile analiz edilmiştir. Elde edilen sonuçlar en başarılı ilk iki algoritmasın güve-alev optimizasyonu ve parçacık sürüsü optimizasyonu olduğunu göstermiştir. Geniş bir veri seti üzerinde çeşitli seviyelerde eşikleme ve farklı sonlandırma kriterleri ile yapılan bu çalışmanın sonuçları, görüntü segmentasyonu alanında çalışan araştırmacılara algoritma seçimi konusunda önemli bilgiler sağlamaktadır.

Keywords

Çok seviyeli eşikleme, Görüntü segmentasyonu, Meta sezgisel optimizasyon

Comparison of multi-level image thresholding performances of meta-heuristic search algorithms

Abstract

Thresholding based segmentation methods are successful in determining small number of threshold values. As the computational complexity is high for multilevel thresholding, meta-heuristic search algorithms are used. In this study, the performance of 11 meta-heuristic search algorithms (artificial bee colony, cuckoo search, differential evolution, gray wolf optimization, harris hawk optimization, moth-flame optimization, particle swarm optimization, sine-cosine algorithm, symbiotic organisms search, salp swarm algorithm, whale optimization algorithm) were compared for image segmentation problem. The low and medium level (m=2,3,4,5,6,7,8) segmentation performances of algorithms were evaluated. The experimental results on 100 images were analyzed with Friedman statistical analysis method. The obtained results showed that the first two most successful algorithms were moth-flame optimization and particle swarm optimization. The results of this study, conducted on a large dataset with various levels of thresholding and different termination criteria, provide important information on algorithm selection for researchers working in the field of image segmentation.

Keywords

Multi level thresholding, Image segmentation, Meta heuristic optimization

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