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k-Means Kümeleme Algoritması ile Renk Tabanlı Segmantasyon ve Renk Uzaylarının Görüntü Niceliklerine Etkisinin Sayısal Analizi

Year 2022, Volume: 9 Issue: 4, 1506 - 1517, 31.12.2022
https://doi.org/10.31202/ecjse.1141148

Abstract

Görüntü işleme uygulamalarında RGB, Lab ve HSV gibi renk uzayları kullanılmaktadır. Renk uzayları bir görüntüye ait piksellerin farklı matematiksel yöntemlerle matris formatında temsil edilmesidir. Bu renk uzayları kullanılarak resmin sayısallaştırılması ve bir matris formatına dönüştürülmesi sağlanmaktadır. Matrisin her bir elemanı görüntüdeki bir piksele karşılık gelmektedir. RGB renk uzayında ki bir resim üç boyutlu ve resmin genişliğinde ve yüksekliğinde kullanılan piksel sayısına karşılık gelen bir matris boyutu ile temsil edilmektedir. Matris üç boyutlu olup birinci boyutta kırmızı(R), ikinci boyutta yeşil(G) ve üçüncü boyutta mavi(B) renk bilgisi değeri yer almaktadır. Benzer olarak diğer renk uzaylarında da benzer matris yapısı kullanılmaktadır. Bu çalışmada bu renk uzaylarının görüntü niceliklerine etkisi uygulamalı ve karşılaştırmalı olarak verilmiştir. Görüntü nicelikleri olarak görüntü içinde bulunan nesne sayısı, nesnelerin piksel sayısı gibi değerler hesaplanmıştır. Görüntülerin sayısallaştırılması ile özniteliklerin tespitinde kullanılan algoritmalar(k-means clustering ) ile sonuçlar farklı renk uzayları için ayrı ayrı elde edilmiştir. Bu hesaplanan değerler RGB, Lab ve HSV renk uzaylarında karşılaştırmalı olarak verilmiştir.

References

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Color Based Segmentation with k-Means Clustering Algorithm and Numerical Analysis of the Effect of Color Spaces on Image Quantities.

Year 2022, Volume: 9 Issue: 4, 1506 - 1517, 31.12.2022
https://doi.org/10.31202/ecjse.1141148

Abstract

Color spaces such as RGB, Lab and HSV are used in image processing applications. Color spaces are the representation of pixels of an image in matrix format using different mathematical methods. By using these color spaces, the image is digitized and converted to a matrix format. In our study, as a method, color-based clustering was performed on the color image by using the "K-Means clustering" algorithm, and the effect of color spaces on image quantities was given in an applied and comparative manner. Values such as the number of objects in the image and the number of pixels of the objects were calculated as image quantities. By digitizing the images, their attributes were obtained separately for different color spaces. These calculated values are given comparatively in RGB, Lab and HSV color spaces.

References

  • [1]. Pathan M., Patel N., Yagnik H., Shah M., "Artificial cognition for applications in smart agriculture: A comprehensive review", Artif. Intell. Agric., 2020, 4, 81–95
  • [2]. Rehman T.U., Mahmud M.S., Chang Y.K., Jin J., Shin J., "Current and future applications of statistical machine learning algorithms for agricultural machine vision systems", Comput. Electron. Agric., 2019, 156, 585–605
  • [3]. Ayoub Shaikh T., Rasool T., Rasheed Lone F., "Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming", Comput. Electron. Agric., 2022, 198, 107119
  • [4]. Barbedo J.G.A., "Detection of nutrition deficiencies in plants using proximal images and machine learning: A review", Comput. Electron. Agric., 2019, 162, 482–492
  • [5]. Altalak M., Uddin M.A., Alajmi A., Rizg A., "Smart Agriculture Applications Using Deep Learning Technologies: A Survey", Appl. Sci., 2022, 12(12),5919
  • [6]. Huang S., Fan X., Sun L., Shen Y., Suo X., "Research on Classification Method of Maize Seed Defect Based on Machine Vision", J. Sensors, 2019
  • [7]. Suchithra M.S., Pai M.L., "Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters", Inf. Process. Agric., 2020, 7(1), 72–82
  • [8]. Liakos K.G., Busato P., Moshou D., Pearson S., Bochtis D., "Machine Learning in Agriculture: A Review", Sensors 2018, Vol. 18, Page 2674, 2018, 18(8), 2674
  • [9]. Atay E., Crété X., Loubet D., Lauri P.E., "Diurnal and Seasonal Growth Responses of Apple Trees to Water-Deficit Stress", Erwerbs-Obstbau, 2022,1-6
  • [10]. Atalay M., Çelik E., "Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamaları", Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 2017, 9(22), 155-172.
  • [11]. Pivoto D., Waquil P.D., Talamini E., Finocchio C.P.S., Dalla Corte V.F., de Vargas Mores G., "Scientific development of smart farming technologies and their application in Brazil", Inf. Process. Agric., 2018, 5(1), 21–32
  • [12]. Zheng X., Lei Q., Yao R., Gong Y., Yin Q., "Image segmentation based on adaptive K-means algorithm", Eurasip J. Image Video Process., 2018, 1, 1–10
  • [13]. Tao M., Ma X., Huang X., Liu C., Deng R., Liang K., vd., "Smartphone-based detection of leaf color levels in rice plants", Comput. Electron. Agric., 2020, 173, 105431
  • [14]. Sinaga K.P., Yang M.S., "Unsupervised K-means clustering algorithm", IEEE Access, 2020, 8, 80716–80727
  • [15]. Cebeci Z., Yıldız F., Kayaalp G., "K-Ortalamalar Kümelemesinde Optimum K Değeri Seçilmesi", 2. Ulus. Yönetim Bilişim Sist. Kongresi, 2015, 231–242
  • [16]. Yuan C., Yang H., "Research on K-Value Selection Method of K-Means Clustering Algorithm", 2019, 2(2), 226–235
  • [17]. Umargono E., Suseno J.E., Gunawan S.. V., "K-Means Clustering Optimization Using the Elbow Method and Early Centroid Determination Based on Mean and Median Formula", 2020, 121–129
  • [18]. Mustafa A.G., İstanbul T., Üniversitesi A., "K-Means Ve Hiyerarşik Kümeleme Algoritmanın Weka Ve Matlab Platformlarında Karşılaştırılması", İstanbul Aydın Üniversitesi Derg., 2019, 11(3), 303–317
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  • [20]. Ajmal A., Hollitt C., Frean M., Al-Sahaf H., "A Comparison of RGB and HSV Colour Spaces for Visual Attention Models", Int. Conf. Image Vis. Comput. New Zeal., 2018, 1-6
  • [21]. MathWorks Inc., “Understanding Color Spaces and Color Space Conversion - MATLAB & Simulink.” https://www.mathworks.com/help/images/understanding-color-spaces-and-color-space-conversion.html?searchHighlight=color space&s_tid=srchtitle_color space_1 (accessed Sep. 24, 2022).
  • [22]. Gowda S.N., Yuan C., "ColorNet: Investigating the Importance of Color Spaces for Image Classification", Lect. Notes Comput. Sci., 2018, 581–596
  • [23]. Ibraheem N.A., Hasan M.M., Khan R.Z., Mishra P.K., "ARPN Journal of Science and Technology:: Understanding Color Models: A Review", ARPN J. Sci. Technol., 2012, 2(3), 265-275
  • [24]. MathWorks I., “Color-Based Segmentation Using K-Means Clustering - MATLAB & Simulink Example.” https://www.mathworks.com/help/images/color-based-segmentation-using-k-means-clustering.html (accessed Sep. 22, 2022).
  • [25]. MathWorks I., “K-means clustering based image segmentation - MATLAB imsegkmeans.” https://www.mathworks.com/help/images/ref/imsegkmeans.html (accessed Sep. 22, 2022).
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Hamit Armağan 0000-0002-8948-1546

Publication Date December 31, 2022
Submission Date July 5, 2022
Acceptance Date September 30, 2022
Published in Issue Year 2022 Volume: 9 Issue: 4

Cite

IEEE H. Armağan, “Color Based Segmentation with k-Means Clustering Algorithm and Numerical Analysis of the Effect of Color Spaces on Image Quantities”., El-Cezeri Journal of Science and Engineering, vol. 9, no. 4, pp. 1506–1517, 2022, doi: 10.31202/ecjse.1141148.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
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