Identifikasi Varietas Koi Berdasarkan Gambar Menggunakan Zero Parameter Simple Linear Iterative Clustering dan Support Vector Machine
(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author
Abstract
There’s currently 120 types of koi fish that has been bred around the world. The types of koi fish depends on the color patterns and shapes they have. There’s alot of patterns that has similarity between one type with another. For example, sanke and showa koi fish will look similar from a non-expert’s point of view, because both type has same color pattern, which is red, black and white. In actuality, sanke koi is dominantly red and white with slight black accent, while showa’s dominant color is red and black, with white accent.
In this research, Zero Parameter Simple Linear Iterative Clustering (SLICO) method and Simple Linear Iterative Clustering (SLIC) will be tested and used to process the image segmentation process to eliminate the background of the image. Color Local Binary Pattern method is used to get the textures on images through the RGB, HSV, and grayscale colorspace. Support Vector Machine is used to identify types of koi fish. To test the SVM, two kind of kernel is used, which is linear kernel and Radial Basis Function (RBF) kernel.
The results of this study are the program able to recognize types of koi from iamges. The test results show an accuracy of 36% in grayscale colorspace, 50% in RGB colorspace, and 48% in HSV colorspace.
Keywords
Full Text:
PDFReferences
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. 2012. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282. doi: 10.1109/tpami.2012.120
Allken, V., Handegard, N. O., Rosen, S., Schreyeck, T., Mahiout, T., & Malde, K. 2018. Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science, 76(1), 342–349. doi: 10.1093/icesjms/fsy147
Banerji, S., Verma, A., & Liu, C. 2012. LBP and Color Descriptors for Image.
Bodic, P. L., Locteau, H., Adam, S., Héroux, P., Lecourtier, Y., & Knippel, A. 2009. Symbol Detection Using Region Adjacency Graphs and Integer Linear Programming. 2009 10th International Conference on Document Analysis and Recognition. doi: 10.1109/icdar.2009.202S
Gonzalez, R. C., & Woods, R. E. 2018. Digital Image Processing (4th, Illustr ed.). Pearson.
Hsu, C., Chang, C., & Lin, C. 2010. “A Practical Guide to Support Vector Classification” . Deptt of Computer Sci. National Taiwan Uni, Taipei, 106, Taiwan URI= http://www.csie.ntu.edu.tw/~cjlin 2007
Kock, S. D., & Gomelsky, B. 2015. Japanese Ornamental Koi Carp: Origin, Variation and Genetics. Biology and Ecology of Carp, 27–53. doi: 10.1201/b18547-4
Maghsoudi, O. H. 2017. Superpixel based segmentation and classification of polyps in wireless capsule endoscopy. 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). doi: 10.1109/spmb.2017.8257027
Malmberg, F. 2011. Graph-based Methods for Interactive Image Segmentation. Uppsala: Acta Universitatis Upsaliensis.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. 2011. Scikit-learn: Machine Learning in {P}ython. Journal of Machine Learning Research, 12.
Rosebrock, A. 2015. Local Binary Patterns with Python & OpenCV. Retrieved from pyimagesearch URI= https://www.pyimagesearch.com/2015/12/07/local-binary-patterns-with-python-opencv/
Shandy, D. 2017. Pengenalan Varietas Ikan Koi Berdasarkan Foto Menggunakan Simple Linear Iterative Clustering Superpixel Segmentation dan Convolutional Neural Networks. Skripsi. Tidak Diterbitkan. Fakultas Teknik Informatika. Sekolah Tinggi Teknik Surabaya; Surabaya.
Shapiro, L., & Stockman, G. 2001. Computer Vision (illustrate). Prentice Hall.
Sharma, H., Alekseychuk, A., Leskovsky, P., Hellwich, O., Anand, R., Zerbe, N., & Hufnagl, P. 2012. Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics. Diagnostic Pathology, 7(1). doi: 10.1186/1746-1596-7-134
Singh, C., Walia, E., & Kaur, K. P. 2018. Color texture description with novel local binary patterns for effective image retrieval. Pattern Recognition, 76, 50–68. https://doi.org/10.1016/j.patcog.2017.10.021
Srivastava, Durgesh & Bhambhu, L.. 2010. Data classification using support vector machine. Journal of Theoretical and Applied Information Technology. 12. 1-7.
Zdimalova, Maria & Z., Kriv,á, & Bohumel, T.. 2015. Graph cuts in image processing. APLIMAT 2015 - 14th Conference on Applied Mathematics, Proceedings. 774-7
Refbacks
- There are currently no refbacks.
Jurnal telah terindeks oleh :
