Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Research Report - Engineering Science JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Engineering and Technological Sciences Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Veronica S. Moertini
Jurusan Teknik Informatika, Fakultas Teknologi Informasi Dan Sains Universitas Katolik Parahyangan, Bandung
Computer Science & IT Dentistry Education Engineering Library & Information Science

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Engineering and Technological Sciences

 1 
Algorithms of Clustering and Classifying Batik Images Based on Color, Contrast and Motif Moertini, Veronica S.; Sitohang, Benhard
Journal of Engineering and Technological Sciences Vol 37, No 2 (2005)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2121.037 KB) | DOI: 10.5614/itbj.eng.sci.2005.37.2.5

Abstract

An interactive system could be provided for batik customers with the aim of helping them in selecting the right batiks. The system should manage a collection of batik images along with other information such as fashion color type, the contrast degree, and motif. This research aims to find methods of clustering and classifying batik images based on fashion color, contrast and motif. A color clustering algorithm using HSV color system is proposed. Two algorithms for contrast clustering, both utilize wavelet, are proposed. Six algorithms for clustering and classifying batik images based on group of motifs, employing shape- and texture-based techniques, are explored and proposed. Wavelet is used in image pre-processing, Canny detector is used to detect image edges. Experiments are conducted to evaluate the performance of the algorithms. The result of experiments shows that fashion color and contrast clustering algorithms perform quite well. Three of motif based clustering and classification algorithms perform fairly well, further work is needed to increase the accuracy and to refine the classification into detailed motif.
Co-Authors Arry Sardi Benhard Sitohang Chandra Wijaya Felix C Saputra Gede Karya Gede Karya Gede Karya Lucky Adhie Oerip S. Santosa Rosa de Lima E. Padmowati Vania Natali