Image Classification Based On Color Using Thresholding Method
Keywords:
Image classification, Thresholding method, Color, Image segmentation, Threshold valueAbstract
This research aims to categorize images based on color using the method of thresholding. Image classification based on color plays a crucial role in various applications such as object detection, traffic monitoring, and medical image processing. The thresholding method is a popular approach used in image segmentation due to its effectiveness and computational efficiency. In this method, grayscale images are converted into binary images by determining a specific threshold value. This research utilizes the thresholding method to separate pixels based on their color intensity. The research methodology consists of several steps, including dataset collection, image pre-processing, color feature extraction, application of the thresholding method, and class labeling. The study's benefits include object recognition, cost and time reduction in image classification, and improved product quality and income for farmers.
References
Al-Ameen, Z., Sulong, G., Rehman, A., & Saba, T. (2021). Image segmentation techniques: A survey of recent developments. Journal of Information Science and Engineering, 37(3), 567–593. https://doi.org/10.6688/JISE.202105_37(3).0002
Cahyadi, M. F., Syahputra, S., & Syari, M. A. (2023). Penerapan Metode Thresholding Pada Proses Transformasi Citra Digital. Educate: Journal Ilmu Pendidikan Dan Pengajaran, 1(3), 319–346. https://doi.org/10.56114/edu.v1i3.422
Ciputra, A., Setiadi, D. R. I. M., Rachmawanto, E. H., & Susanto, A. (2018). Klasifikasi Tingkat Kematangan Buah Apel Manalagi Dengan Algoritma Naive Bayes Dan Ekstraksi Fitur Citra Digital. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 465–472. https://doi.org/10.24176/simet.v9i1.2000
Gill, H. S., Khalaf, O. I., Alotaibi, Y., Alghamdi, S., & Alassery, F. (2022). Fruit Image Classification Using Deep Learning. Computers, Materials and Continua, 71(2), 5135–5150. https://doi.org/10.32604/cmc.2022.022809
Heryanto, I. W. A., Artama, Kurniawan, M. W. S., & Gunadi, G. A. (2020). Segmentasi Warna dengan Metode Thresholding. Wahana Matematika Dan Sains, 14(1), 54–64.
Hoang, N. D. (2020). Image Processing-Based Pitting Corrosion Detection Using Metaheuristic Optimized Multilevel Image Thresholding and Machine-Learning Approaches. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/6765274
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2021). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Kumar, R., Singh, D., & Kaur, M. (2022). Fruit recognition using HSV color space and machine learning techniques. Multimedia Tools and Applications, 81, 17751–17769. https://doi.org/10.1007/s11042-021-11632-w
Nasution, A. M. T., & Amrullah, S. A. (2022). Simple Vision System for Apple Varieties Classification. Industria: Jurnal Teknologi Dan Manajemen Agroindustri, 11(1), 51–63. https://doi.org/10.21776 ub.industria.2022.011.01.6
Nurfadhilah, A., Huda, M., & Suryani, I. (2021). Implementation of color-based thresholding for real-time waste classification using a camera sensor. IOP Conference Series: Materials Science and Engineering, 1051(1), 012050. https://doi.org/10.1088/1757-899X/1051/1/012050
Rabbani, H. A., Rahman, M. A., & Rahayudi, B. (2021). Perbandingan Ruang Warna RGB dan HSV dalam Klasifikasi Kematangan Biji Kopi. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(6), 2243–2248. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/9013
Ramadan, I., Syauqy, D., & Primananda, R. (2021). Sistem Pendeteksi Kematangan Buah Apel menggunakan Metode Naïve Bayes berbasis Embedded System. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(4), 1654–1661. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/8959
Razabni, D., Medinah, E., & Sinurat, S. (2020). Analisa dan Perbandingan Algoritma Otsu Thresholding dengan Algoritma Region Growing Pada Segmentasi Citra Digital. Journal of Computer System and Informatics (JoSYC), 2(1), 9–16.
Saputra, A. (2019). Klasifikasi Pengenalan Buah Menggunakan Algoritma Naive Baiyes. Jurnal RESISTOR (Rekayasa Sistem Komputer), 2(2), 83–88. https://doi.org/10.31598/jurnalresistor.v2i2.434
Sari, I. E. Y., Furqan, M., & Sriani, S. (2020). Penerapan Metode Otsu dalam Melakukan Segmentasi Citra pada Citra Naskah Arab. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 20(1), 59–72.
https://doi.org/10.30812/matrik.v20i1.658
Sumari, A. D. W., Marwani, P. I., & Syulistyo, A. R. (2021). Klasifikasi Mutu Telur Burung Puyuh Berdasarkan Warna Dan Tekstur Menggunakan Metode K- Nearest Neighbor ( Knn ) Dan Fusi Classification of the Quality Quail Eggs Based on Color and Texture Using K-Nearest Neighbor ( Knn ) Method and Information. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIK), 8(5), 1019–1028. https://doi.org/10.25126/jtiik.202184393
Suradi, A. A. M., Rasyid, M. F., Mushaf, M., & Rizal, M. (2023). Deteksi Tingkat Kematangan Buah Apel Menggunakan Segmentasi Ruang Warna HSV. Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, XII(1), 19–26.
Winoto, Y. (2019). Rancang Bangun Model Pengklasifikasian Bahan Pustaka Berbasis Warna. Khizanah Al-Hikmah : Jurnal Ilmu Perpustakaan, Informasi, Dan Kearsipan, 7(1), 56. https://doi.org/10.24252/kah.v7i1a5
Zhang, Y., Zhang, X., & Du, B. (2020). Color image classification using histogram of color features in different color spaces. Pattern Recognition Letters, 133, 38–45. https://doi.org/10.1016/j.patrec.2020.02.015
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