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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2018

23.04.2015 | Original Article

Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms

verfasst von: Sushil Kumar, Millie Pant, Manoj Kumar, Aditya Dutt

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2018

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Abstract

Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour intensity which makes segmentation very challenging. In this paper we suggest a fitness function based on pixel-by-pixel values and optimize these values through evolutionary algorithms like differential evolution (DE), particle swarm optimization (PSO) and genetic algorithms (GA). The corresponding variants are termed GA-SA, PSO-SA and DE-SA; where SA stands for Segmentation Algorithm. Experimental results show that DE performed better in comparison of PSO and GA on the basis of computational time and quality of segmented image.
Vorheriger Artikel Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization

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Metadaten
Titel
Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms
verfasst von
Sushil Kumar
Millie Pant
Manoj Kumar
Aditya Dutt
Publikationsdatum
23.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0360-7

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