Butterfly Feature Extraction Using HSV, Lacunarity, and CNN

Putri Nur Rahayu, Friska Intan Sukarno, Immanuel Freddy Augustino, R. A. Norromadani Yuniati, Ardhon Rakhmadi

Sari


This study aims to extract the morphological features of butterflies using the HSV (Hue, Saturation, Value) and lacunarity. The HSV method is used to obtain color information from butterfly images. lacunarity is used to extract texture characteristic to enhance the visual representation of the object. These extracted features are used as input for the processing of classification using algorithm of Convolution Neural Network (CNN). Based on the experimental result, the classification has accuracy 70%. This accuracy indicates that the combination of HSV and lacunarity methods is sufficiently effective in describing of the visual butterflies features for automatic classification.


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Referensi


K. Hsv, “Combination of HSV and Lacunarity for Feature Extraction on Butterfly Dataset,†pp. 1–8.

O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, “Tomatoes classification using K-NN based on GLCM and HSV color space,†Proc. - 2017 Int. Conf. Innov. Creat. Inf. Technol. Comput. Intell. IoT, ICITech 2017, vol. 2018-Janua, no. November, pp. 1–6, 2018, doi: 10.1109/INNOCIT.2017.8319133.

I. Kurniastuti, E. N. I. Yuliati, F. Yudianto, and T. D. Wulan, “Determination of Hue Saturation Value (HSV) color feature in kidney histology image,†J. Phys. Conf. Ser., vol. 2157, no. 1, 2022, doi: 10.1088/1742-6596/2157/1/012020.

W. S. Sari and C. A. Sari, “Klasifikasi Bunga Mawar Menggunakan Knn Dan Ekstraksi Fitur Glcm Dan Hsv,†Skanika, vol. 5, no. 2, pp. 145–156, 2022, doi: 10.36080/skanika.v5i2.2951.

A. Mohan and J. Peeples, “Lacunarity Pooling Layers for Plant Image Classification using Texture Analysis,†pp. 5384–5392, 2024, doi: 10.1109/CVPRW63382.2024.00547.

J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,†Agric., vol. 11, no. 8, pp. 1–18, 2021, doi: 10.3390/agriculture11080707.

Y. Zhao and H. Zhang, “Quantitative Effectiveness Assessment and Role Categorization of Individual Units in Convolutional Neural Networks,†2021, [Online]. Available: http://arxiv.org/abs/2103.09716

M. Alrahhal and K. P. Supreethi, “Enhancing image retrieval accuracy through multi-resolution HSV-LNP feature fusion and modified K-NN relevance feedback,†Int. J. Inf. Technol., no. June, 2024, doi: 10.1007/s41870-024-02000-y.

Mohammad Yazdi Pusadan, Indah Safitri, and Wirdayanti, “The Image Extraction Using the HSV Method to Determine the Maturity Level of Palm Oil Fruit with the k-nearest Neighbor Algorithm,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 6, pp. 1448–1456, 2023, doi: 10.29207/resti.v7i6.5558.

P. N. Rahayu, “Extraction of Timber ’ s Features using Color Moment , GLCM , Lacunarity , and Isotropic Undecimated Wavelet Transform with MLP Classificationâ€.

B. Abirami, K. K.-J. of P. Negative, and undefined 2023, “A Deep Supervised Lacunarity Analysis Model Based on Weight Adaptive Local Binary Pattern Texture For Palmprint Recognition System,†Pnrjournal.Com, vol. 14, no. 3, p. 2023, 2023, doi: 10.47750/pnr.2023.14.03.258.

D. Erwanto, P. N. Rahayu, and Y. B. Utomo, “Classification of Defects on Packing Cans Using Lacunarity and Naïve Bayes Methods,†J. Elektro Lucea, vol. 7, no. 2, pp. 142–150, 2021.

M. K. Anam, S. Sumijan, K. Karfindo, and M. B. Firdaus, “Comparison Analysis of HSV Method, CNN Algorithm, and SVM Algorithm in Detecting the Ripeness of Mangosteen Fruit Images,†Indones. J. Artif. Intell. Data Min., vol. 7, no. 2, p. 348, 2024, doi: 10.24014/ijaidm.v7i2.29739.

G. Science and E. Outlook, “何霄嘉 1 ，郑大玮 2 ，许åŸéš† 3 （1.,†vol. 32, no. 2, pp. 58–65, 2020.

S. Rachmawati, A. E. Minarno, and Y. Azhar, “Klasifikasi Lesi Kulit Menggunakan Convolutional Neural Network,†J. Repos., vol. 4, no. 3, pp. 363–372, 2024, doi: 10.22219/repositor.v4i3.32327.

S. Deepak and P. M. Ameer, “Automated Categorization of Brain Tumor from MRI Using CNN features and SVM,†J. Ambient Intell. Humaniz. Comput., vol. 12, no. 8, pp. 8357–8369, 2021, doi: 10.1007/s12652-020-02568-w.




DOI: http://dx.doi.org/10.30811/jaise.v5i2.6876

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