Statistical Modeling of Gabor Filtered Magnitude for Batik Image Retrieval

Authors

  • Heri Prasetyo Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.
  • Wiranto Wiranto Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.
  • Winarno Winarno Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

Keywords:

Batik, Gabor Filtered, Image Retrieval, Magnitude Component,

Abstract

This paper presents an effective and efficient way on statistical modeling of Gabor filtered magnitude to generate an image feature descriptor. Two statistical distributions, i.e. Gaussian and Rayleigh distributions, are considered in the image feature extraction. The image feature is simply constructed by concatenating the distribution estimators of Gabor filtered magnitudes under different scales and orientations. As documented in the experimental section, the proposed method yields good performance in the Batik image retrieval system. In addition, the performance of Gabor feature can be improved by injecting the color feature in order to capture the color richness of an image.

References

S. Bhagavathy, J. Tesic, and B. S. Manjunath, “On the Rayleigh nature of Gabor filter outputs,” in Proc. IEEE Int. Conf. Image Process., Nov. 2003, DOI: 10.1109/ICIP.2003.1247352.

C. Liu, and H. Wechsler, “Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition,” IEEE Trans. Image Process., vol. 11, no. 4, pp. 467-476, Apr. 2002.

B. S. Oh, K. Oh, A. B. J. Teoh, Z. Lin, and K. A. Toh, “A Gabor-based network for heterogeneous face recognition,” Neurocomputing, Vol. 261, pp. 253-265, Oct. 2017.

T. O jala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariance texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 7, pp. 971-987, 2002.

X. Tan, and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1635-1650, 2010.

Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE Trans. Image Process., vol. 23, no. 7, pp. 1657-1663, 2010.

B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor,” IEEE Trans. Image Process., vol. 19, no. 2, pp. 533-544, 2010.

P. Liu, J. M. Guo, K. Chamnongthai, and H. Prasetyo, “Fusion of color histogram and LBP-based features for texture image retrieval and classification,” Information Sciences, vol. 390, pp. 95- 111, Jun. 2017.

J. M. Guo, H. Prasetyo, H. Lee, and C. C. Yao, “Image retrieval using indexed histogram of Void-and-Cluster Block Truncation Coding,” Signal Process., vol. 123, pp. 143-156, Jun. 2016.

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Published

2018-07-03

How to Cite

Prasetyo, H., Wiranto, W., & Winarno, W. (2018). Statistical Modeling of Gabor Filtered Magnitude for Batik Image Retrieval. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 85–89. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4322

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