Batik Image Retrieval Using ODBTC Feature and Particle Swarm Optimization

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.
  • Umi Salamah Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.
  • Bambang Harjito Department of Informatics, Universitas Sebelas Maret (UNS), Surakarta, Indonesia.

Keywords:

Batik, Image Retrieval, Particle Swarm Optimization, Similarity Weighting Constants,

Abstract

This paper proposes an effective and efficient approach to Batik image retrieval using Ordered Dither Block Truncation Coding (ODBTC) feature. Similarity degree between two images can be easily investigated under similarity distance score between their feature descriptors. As documented in the experimental section, the feature descriptor outperforms the former existing schemes under Batik image database. The Particle Swarm Optimization (PSO) iteratively searches the optimal similarity weighting constants to further improve the image retrieval performance. Thus, a set of retrieved images become more satisfactory and acceptable for user desire and preference.

References

J. M. Guo, H. Prasetyo, and H. S. Su, “Image indexing using the color and bit pattern feature fusion,” Journal of Visual Communication and Image Representation, vol. 24, no. 8, pp. 1360-1379, 2013.

J. M. Guo, and H. Prasetyo, “Content-based image retrieval using features extracted from halftoning-based block truncation coding,” IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 1010-1024, Mar. 2015.

J. M. Guo, H. Prasetyo, H. Lee, and C. C. Yao, “Image retrieval using indexed histogram of void-and-cluster block truncation coding,” Signal Processing, vol. 123, pp. 143-156, June 2016.

T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariance texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 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 Transactions on Image Processing, 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 Transactions on Image Processing, 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 Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010.

J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE International Conference on Neural Network, Perth, Australia, 1995, pp. 1941–1948.

M. Clerc and J. Kennedy, “The particle swarm—Explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, Feb. 2002.

Downloads

Published

2018-07-03

How to Cite

Prasetyo, H., Wiranto, W., Winarno, W., Salamah, U., & Harjito, B. (2018). Batik Image Retrieval Using ODBTC Feature and Particle Swarm Optimization. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 71–74. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4319

Most read articles by the same author(s)