Aerial Image Segmentation with Clustering Using Fireworks Algorithm

Authors

  • Muhammad Hariz Arasy Telkom University
  • Suyanto Suyanto School of Computing, Telkom University
  • Kurniawan Nur Ramadhani School of Computing, Telkom University

DOI:

https://doi.org/10.21108/INDOJC.2019.4.1.245

Abstract

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

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Published

2019-03-22

How to Cite

Arasy, M. H., Suyanto, S., & Ramadhani, K. N. (2019). Aerial Image Segmentation with Clustering Using Fireworks Algorithm. Indonesian Journal on Computing (Indo-JC), 4(1), 19–28. https://doi.org/10.21108/INDOJC.2019.4.1.245

Issue

Vol. 4 No. 1 (2019): Maret, 2019

Section

Computer Science

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