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

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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

Issue

Vol. 10 No. 2-4: Enhancing Digital Trust for Sustainable Development

Section

Articles

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