Performance comparison analysis features extraction methods for Batik recognition

Author

Nurhaida, Ida ; Manurung, Ruli ; Arymurthy, Aniati Murni

Author_Institution

Lab. of Pattern Recognition & Image Process., Univ. of Indonesia, Depok, Indonesia

fYear

2012

fDate

1-2 Dec. 2012

Firstpage

207

Lastpage

212

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%.

Keywords

Gabor filters; edge detection; feature extraction; history; image classification; image recognition; GLCM features; Gabor filters; Indonesia; batik motifs recognition; canny edge detection; classification accuracy; cultural heritage; digital images; feature extraction methods; gray level co-occurrence matrices; performance comparison analysis; testing level accuracy; Accuracy; Filter banks; Gabor filters; Image edge detection; Noise; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computer Science and Information Systems (ICACSIS), 2012 International Conference on

Conference_Location

Depok

Print_ISBN

978-1-4673-3026-8

Type

conf

Filename

6468767