GLCM Based Locally Feature Extraction On Natural Image

Authors

  • Edi Faisal Universitas Dian Nuswantoro
  • Agung Nugroho Universitas Dian Nuswantoro
  • Ruri Suko Basuki Universitas Dian Nuswantoro
  • Suharnawi Suharnawi Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v7i2.6569

Abstract

GLCM is a feature extraction method that uses statistical analysis using a gray scale. Contrast, correlation, energy and entropy are feature features whose value will be sought as the basis for finding the threshold which can then be used to find the threshold value in image segmentation. In this study, a local-based GLCM method is used where the image that has been made into grayscale will be divided into 16 parts of the same size. Each section will look for the value of its GLCM features, namely Contrast, correlation, energy and entropy. The calculation of these four features will be applied to 16 parts of the grayscale image, which can then be used to find the threshold value. The results of the four features in the calculation with an angle of 0o are the contrast value = 0.0080, correlation = 0.619, energy : 0.00160 and entropy : 0.05591.

Author Biography

Edi Faisal, Universitas Dian Nuswantoro

TSI-S1

References

J. Wang and M. F. Cohen, “Image and Video Matting: A Survey,” Found. Trends® Comput. Graph. Vis., vol. 3, no. xx, pp. 97–175, 2007.

A. Levin, A. Rav-Acha, and D. Lischinski, “Spectral matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1699–1712, 2008.

A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 228–242, 2008.

E. Shahrian, D. Rajan, B. Price, and S. Cohen, “Improving image matting using comprehensive sampling sets,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 636–643, 2013.

J. Wang and M. F. Cohen, “Optimized color sampling for robust matting,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2007.

X. Bai and G. Sapiro, “Geodesic matting: A framework for fast interactive image and video segmentation and matting,” Int. J. Comput. Vis., vol. 82, no. 2, pp. 113–132, 2009.

H. Liu, L. Ma, X. Cai, Z. Chen, and Y. Shen, “A closed-form solution to video matting of natural snow,” Inf. Process. Lett., vol. 109, no. 18, pp. 1097–1104, 2009.

R. S. Basuki, “Fuzzy C-Means Algorithm for Adaptive Threshold on Alpha Matting,” no. July, pp. 177–180, 2012.

N. Zayed and H. A. Elnemr, “Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities,” Int. J. Biomed. Imaging, vol. 2015, 2015.

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Published

2022-09-07

Issue

Vol. 7 No. 2 (2022): Journal of Applied Intelligent System

Section

Articles

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