Segmentasi Citra Bunga Menggunakan Blob Analisis
- Ines Hediani Ikasari Universitas Pamulang, Tangerang Selatan, Indonesia
- Resti Amalia Universitas Pamulang, Tangerang Selatan, Indonesia
-
Perani Rosyani
*
Universitas Pamulang, Tangerang Selatan,
Indonesia
(*) Corresponding Author
Abstract
Content Based Image Retrieval (CBIR) for image segmentation is a concern this year, especially in the development of computer vision. The object discussed in this study is about interest, which uses a dataset from ImageCLEF2017 by taking 8 flower samples. Image of flowers in the dataset is still a lot of noise such as the initial background behind objects such as leaves, tree trunks or others. So we need a method to eliminate the noise, this method for cleaning noise is done by color clusters using the K-means method. By color clustering using K-Means and using color clusters k=2,3,4,and5. After that, a morphological process is carried out in order to obtain a clean area so that it can be compared with the original image and the Blob values formed. Blob analysis is calculated after the process of cleaning the noise is done in order to get the best value in the process of recognition of images with objects of interest. The results of the segmentation process that have been done are the highest MSE and RMSE values are at k-means results with k=4, while for PNSR are at k=2, and for the lowest MSE and RMSE values are at k=5, while the lowest PNSR is at k=4
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References
Y. Li, J. Zhang, P. Gao, L. Jiang, and M. Chen, “Grab Cut Image Segmentation Based on Image Region,” 2018 IEEE 3rd Int. Conf. Image, Vis. Comput., pp. 311–315, 2018.
K. Jiao and Z. Pan, “A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm,” IEEE Access, vol. 7, pp. 21310–21330, 2019.
G. B. Adhi and I. D. Wahyono, “Segmentasi Gambar Warna Menggunakan Sauvola Modifikasi Fuzzy C-Means (Smfcm),” Lontar Komput., vol. 5, no. 2, pp. 416–423, 2015.
T. Arifin, “Analisa Perbandingan Metode Segmentasi Citra Pada Citra Mammogram,” J. Inform., vol. 3, no. 2, pp. 156–163, 2016.
T. X. Pham, P. Siarry, and H. Oulhadj, “Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation,” Appl. Soft Comput. J., vol. 65, pp. 230–242, 2018.
W. Tan, T. Sunday, and Y. Tan, “Enhanced ‘ GrabCut ’ Tool with Blob Analysis in Segmentation of Blooming Flower Images,” 2013.
P. Rosyani, “Pengenalan Wajah Menggunakan Metode Principal Component Analysis (PCA) dan Canberra Distance,” J. Inform. Univ. Pamulang, vol. 2, no. 2, p. 118, 2017.
J. Lazić, “Image Processing and Computer Vision with MATLAB and SIMULINK,” 2015.
N. L. W. S. R. Ginantra, “Deteksi Batik Parang Menggunakan Fitur Co-Occurence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 7, no. 1, p. 40, 2016.
Atina, “Segmentasi Citra Paru Menggunakan Metode k-Means Clustering,” Segmentasi Citra Paru Menggunakan Metod. k-Means Clust., vol. 3, no. 2, pp. 57–65, 2017.
P. Rosyani, M. Taufik, A. A. Waskita, and D. H. Apriyanti, “Comparison of color model for flower recognition,” 2018 3rd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng., pp. 10–14, 2019.
J. Dong, X. Qu, and H. Li, “Color tattoo segmentation based on skin color space and K-mean clustering,” ICCSS 2017 - 2017 Int. Conf. Information, Cybern. Comput. Soc. Syst., pp. 53–56, 2017.
D. H. Apriyanti, A. M. Arymurthy, and L. T. Handoko, “Identification of orchid species using content-based flower image retrieval,” Proceeding - 2013 Int. Conf. Comput. Control. Informatics Its Appl. “Recent Challenges Comput. Control Informatics”, IC3INA 2013, no. March 2015, pp. 53–57, 2013.
K. Thwe and M. Han, “A Survey of Blob Detection Algorithms for Biomedical Images,” vol. 2016, pp. 0–3, 2016.
S. Ait-Aoudia, E. H. Guerrout, and R. Mahiou, “Medical image segmentation using particle swarm optimization,” Proc. Int. Conf. Inf. Vis., pp. 287–291, 2014.
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