Performance comparison analysis features extraction methods for Batik recognition

Ida Nurhaida, Ruli Manurung, Aniati Murni Arymurthy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)

Abstract

Batik, as a cultural heritage from Indonesia, has a lot of motifs based on certain patterns. This paper discusses feature extraction methods for the recognition of batik motifs in digital images. In this study, the use of several feature extraction methods have been compared in terms of their performance with several scenarios for testing level accuracy. The methods include Gray Level Co-occurrence Matrices (GLCM), Canny Edge Detection, and Gabor filters. The experimental results show that the use of GLCM features has performed the best with a classification accuracy reaching 80%.

Original languageEnglish
Title of host publication2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
Pages207-212
Number of pages6
Publication statusPublished - 1 Dec 2012
Event2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Depok, Indonesia
Duration: 1 Dec 20122 Dec 2012

Publication series

Name2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings

Conference

Conference2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
CountryIndonesia
CityDepok
Period1/12/122/12/12

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