Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means


  • Perani Rosyani * Mail Universitas Pamulang, Tangerang Selatan, Indonesia
  • A Suhendi Universitas Pamulang, Tangerang Selatan, Indonesia
  • D H Apriyanti LIPI, Jawa Timur, Indonesia
  • A A Waskita PPIKSN-BATAN, Tangerang Selatan, Indonesia
  • (*) Corresponding Author
Keywords: Flower Image Segmentation; K-Means; Fuzzy C-Means; Color Features; Blob Analysis; Hausdorff Distance

Abstract

A more detail investigation of color feature for flower segmentation using K-means and fuzzy C-means was conducted in this paper. The sample images containing 1, 2, 3, 4 dianthus del- toides L flowers, obtained from ImageCLEF 2017 will be used. K-means and fuzzy C-means will use different color model components as the feature for segmenting the flower objects from their background while keeping the value of k for K-means and fuzzy C-means constant. Then the performance of the segmentation approaches will be evaluated by using the ground truth infor- mation. The evaluation parameters involved are Hausdorff distance and a number of classifier performance metrics such as accuracy, error rate, sensitivity and specivicity. It is shown that the segmentation process will greatly influenced by the use of LAB color model components

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References

K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask R-CNN,” CoRR, vol. abs/1703.06870, 2017. [Online]. Available: http://arxiv.org/abs/1703.06870

P. Rosyani, M. Taufik, A. A. Waskita, and D. H. Apriyanti, “Comparison of color model for flower recognition,” in 2018 3rd International Conference on Information Technology, Infor- mation System and Electrical Engineering (ICITISEE), Nov 2018, pp. 10–14.

J. Lv, F. Wang, L. Xu, Z. Ma, and B. Yang, “A segmentation method of bagged green apple image,” Scientia Horticulturae, vol. 246, pp. 411 – 417, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0304423818308112

R. Xiang, “Image segmentation for whole tomato plant recognition at night,” Computers and Electronics in Agriculture, vol. 154, pp. 434 – 442, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169918309268

U.-O. Dorj, M. Lee, and S. seok Yun, “An yield estimation in citrus or- chards via fruit detection and counting using image processing,” Computers and Electronics in Agriculture, vol. 140, pp. 103 – 112, 2017. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169916312455

Y. Yu, K. Zhang, L. Yang, and D. Zhang, “Fruit detection for strawberry har- vesting robot in non-structural environment based on mask-rcnn,” Computers and Electronics in Agriculture, vol. 163, p. 104846, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169919301103

J. P. Kumar and S. Domnic, “Image based leaf segmentation and counting in rosette plants,” Information Processing in Agriculture, vol. 6, no. 2, pp. 233 – 246, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2214317318301562

Z. Wang, K. Wang, F. Yang, S. Pan, and Y. Han, “Image segmentation of overlapping leaves based on chan–vese model and sobel operator,” Information Processing in Agriculture, vol. 5, no. 1, pp. 1 – 10, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2214317317301270

E. Zagrouba, S. B. Gamra, and A. Najjar, “Model-based graph-cut method for automatic flower segmentation with spatial constraints,” Image and Vision Computing, vol. 32, no. 12, pp. 1007 – 1020, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0262885614001413

S. Inthiyaz, B. Madhav, and P. Kishore, “Flower image segmentation with pca fused colored covariance and gabor texture features based level sets,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 3277 – 3291, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2090447918300078

N. Sabri, Z. Ibrahim, and N. N. Rosman, “K-means vs. fuzzy c-means for segmentation of orchid flowers,” in 2016 7th IEEE Control and System Graduate Research Colloquium (ICSGRC), Aug 2016, pp. 82–86.

W. Tan, T. Sunday, and Y. Tan, “Enhanced “grabcut” tool with blob analysis in segmentation of blooming flower images,” in 2013 10th International Conference on Electrical Engineer- ing/Electronics, Computer, Telecommunications and Information Technology, May 2013, pp. 1–4.

K. Thorp and D. Dierig, “Color image segmentation approach to monitor flowering in lesquerella,” Industrial Crops and Products, vol. 34, no. 1, pp. 1150 – 1159, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0926669011001129

“Imageclef / lifeclef - multimedia retrieval in clef,” 2017. [Online]. Available: https://www.imageclef.org/2017

“Gimp - gnu image manipulation program,” accessed: August 20, 2019. [Online]. Available: https://www.gimp.org/

A. A. Taha and A. Hanbury, “An efficient algorithm for calculating the exact hausdorff distance,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 11, pp. 2153–2163, Nov. 2015. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2015.2408351

J. Li, S. Tang, H. Zhang, Z. Li, W. Deng, C. Zhao, L. Fan, G. Wang, J. Liu, P. Yin, G. Xu, L. Zhang, and P. Tang, “Clustering of morphological fracture lines for identifying intertrochanteric fracture classification with hausdorff distance–based k- means approach,” Injury, vol. 50, no. 4, pp. 939 – 949, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0020138319301329

J. Han, M. Kamber, and J. Pei, “8 - classification: Basic concepts,” in Data Mining (Third Edition), third edition ed., ser. The Morgan Kaufmann Series in Data Management Systems,

J. Han, M. Kamber, and J. Pei, Eds. Boston: Morgan Kaufmann, 2012, pp. 327 – 391. [On- line]. Available: http://www.sciencedirect.com/science/article/pii/B9780123814791000083


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Article History
Submitted: 2021-12-19
Published: 2021-12-31
Abstract View: 26 times
PDF Download: 5 times
How to Cite
Rosyani, P., Suhendi, A., Apriyanti, D. H., & Waskita, A. A. (2021). Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means. Building of Informatics, Technology and Science (BITS), 3(3), 253-259. https://doi.org/10.47065/bits.v3i3.1060
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