Segmentasi Citra menggunakan Support Vector Machine (SVM) dan Ellipsoid Region Search Strategy (ERSS) Arimoto Entropy berdasarkan Ciri Warna dan Tekstur

Authors

  • Lukman Hakim Teknik Informatika, Fakultas Teknik, Universitas Yudharta, Pasuruan
  • Siti Mutrofin Sistem Informasi, Fakultas Teknik, Universitas Pesantren Tinggi Darul Ulum (Unipdu), Jombang
  • Evy Kamilah Ratnasari Teknik Informatika, Fakultas Teknik, Universitas Dr. Soetomo, Surabaya

DOI:

https://doi.org/10.26594/register.v2i1.440

Abstract

Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM) yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik. Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri. Abstract Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM) classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity. Firstly, the pixel-level color feature and texture feature of the image, which is used as input to SVM model (classifier), are extracted via the local homogeneity and Gray Level Co-Occurrence Matrix (GLCM). Then, determine class of classifier using Arimoto based ERSS thresholding. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation result less satisfied segmented image with 69 % accuracy. Feature reduction is needed to get an effective image segmentation. Key word: image segmentation, support vector machine, ERSS Arimoto Entropy, feature extraction.

References

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Ilea, D. E., & Whelan, P. F. (2011). Image Segmentation Based on The Integration of Colour??Texture. Pattern Recognition, 44(10), 2479??2501.

Liu, Y., & Li, S. (2010). Two-Dimensional Arimoto Entropy Image Thresholding based on Ellipsoid Region Search Strategy. International Conference 2010 on Multimedia Technology (ICMT) . Ningbo.

Wang, X. Y., Wang, T., & Bu, J. (2011). Color Image Segmentation Using Pixel Wise Support Vector Machine Classification. Pattern Recognition, 44(4), 777??787.

Yang, H. Y., Wang, X. Y., Wang, Q. Y., & Zhang, X. J. (2012). LS-SVM Based Image Segmentation Using Color and Texture Information. Journal of Visual Communication and Image Representation, 23(7), 1095??1112.

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Published

2016-01-01

How to Cite

[1]
L. Hakim, S. Mutrofin, and E. K. Ratnasari, “Segmentasi Citra menggunakan Support Vector Machine (SVM) dan Ellipsoid Region Search Strategy (ERSS) Arimoto Entropy berdasarkan Ciri Warna dan Tekstur”, regist. j. ilm. teknol. sist. inf., vol. 2, no. 1, pp. 11–16, Jan. 2016.

Issue

Vol. 2 No. 1 (2016): January

Section

Article

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