DOI: https://doi.org/10.21456/vol6iss1pp1-10

Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM)

*Neneng Neneng  -  STMIK Teknokrat
Kusworo Adi  -  Universitas Diponegoro
Rizal Isnanto  -  Universitas Diponegoro

Citation Format:
Abstract

Texture is one of the most important features for image analysis, which provides informations such as the composition of texture on the surface structure, changes of the intensity, or brightness. Gray level co-occurence matrix (GLCM) is a method that can be used for statistical texture analysis. GLCM has proven to be the most powerful texture descriptors used in image analysis. This study uses the four-way GLCM 0o, 45o, 90o, and 135o. Support vector machine (SVM) is a machine learning that can be used for image classification. SVM has a high generalization capability without any requirement of additional knowledge, even with the high dimension of the input space. The data used in this study are the image of goat meat, buffalo meat, horse meat, and beef with shooting distance 20 cm, 30 cm and 40 cm. The result of this study shows that the best recognition rate of 87.5% was taken at a distance of 20 cm with neighboring pixels distance d = 2 in the direction GLCM 135o.

Fulltext View|Download
Keywords: Citra; GLCM; Tekstur; SVM.

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Rancang Bangun Tata Kelola Kinerja Perguruan Tinggi Menggunakan IT Balanced Scorecard Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah Metode Forward Chaining Untuk Penentuan Kelayakan Bisnis Usaha Mikro More recent articles
  1. Basset, O., Buquet, B., Abouelkaram, S., Delachartre, P., dan Culioli, J., 2000. Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69, 437-445
  2. Bharati, M. H., Liu, J. J. and Mac Gregor, J. F., 2004. Image texture analysis: methods and comparisons. Chemometrics and Intelligence Laboratory Systems, 72, 57–71
  3. Burges, C.J., 1998. A Tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 1–43
  4. Haralick, R.M., 1979. Statistical and structural approaches to texture. Proceedings of the IEEE, 67, 786–804
  5. Haralick, R.M., Shanmugam, K., dan Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621
  6. Kadir, A., dan Susanto, A., 2012. Teori dan aplikasi pengolahan citra, Andi: Yogyakarta
  7. Kadir, A. dan Susanto, A., 2013. Teori dan Aplikasi Pengolahan Citra, Andi: Yogyakarta
  8. Kudo, M. and Sklansky. J., 2000. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33, 25-41
  9. Krutz, G.W., Gibson, H.G., Cassens, D.L. and Zhang, M., 2000. Colour vision in forest and wood engineering. Landwards, 55, 2–9
  10. Nello, C., dan Taylor, J.S., 2000. An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press
  11. Patel, D., Davies, E.R. and Hannah, I, 1996. The use of convolution operators for detecting contaminants in food images. Pattern Recognition, 29 (6), 1019–1030
  12. Prasetyo, E., 2012. Data Mining Konsep dan Aplikasi Menggunkan Matlab, Andi: Yogyakarta
  13. Prasetyo, E., 2014. Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Andi Publisher: Yogyakarta
  14. Salat, R. dan Osowski, S., 2004. Accurate fault location in the power transmission line using support vector machine approach, IEEE Trans. Power Syst., 19, 879–886
  15. Seetha, M., Muralikrishna, I.V., Deekshatulu, B.L., Malleswari, B.L., Nagaratna, Hedge, P., 2008. Artificial neural network and other methods of image classification. Journal of Theoretical and Applied Information Technology, 1039–1053
  16. Schwartz. W, Pedrini. H, 2006. Textured image segmentation based on spatial dependence usinga Markov random field model, IEEE International Conference on Image Processing, Atlanta, GA, USA, pp. 2449–2452
  17. Sun, D-W., 2000. Inspecting pizza topping percentage and distribution by a computer vision method. Journal of Food Engineering, 44, 245–249
  18. Siqueira, F.R.D., Schwartz, W.R., Pedrini, H., 2013. Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing, 120, 336–345
  19. Zheng, C., Sun, D-W., dan Zheng, L., 2006. Recent applications of image texture for evaluation of food qualities—a review. Trends in Food Science & Technology, 17, 113–128

Last update:

No citation recorded.

Last update:

No citation recorded.