Automatic Indonesian's Batik Pattern Recognition Using SIFT Approach

Ida Nurhaida, Ary Noviyanto, Ruli Manurung, Aniati Murni Arymurthy

Research output: Contribution to journalConference articlepeer-review

25 Citations (Scopus)

Abstract

Batik is a traditional clothwith unique patterns applied to fabric using a wax-resist dyeing technique. Aside from preserving this rich cultural heritage, the automated recognition of Batik patterns would enable many interesting applications. This paper introduces an approach to batik pattern recognition using the Scale Invariant Feature Transform (SIFT) as a feature extraction method. The challenging issues that arise are due to the highly symmetrical and repetitive properties of batik patterns. The Hough transform, as an evidence-based method of object detection, is applied to handle mismatched keypoints resulting from symmetrical and repetitive patterns of batik. On a collection of 120 batik images generated from 20 basic batik patterns, the proposed method showsan improvement over the original SIFT matching method with an equal error rate of 8.47%.

Original languageEnglish
Pages (from-to)567-576
Number of pages10
JournalProcedia Computer Science
Volume59
DOIs
Publication statusPublished - 1 Jan 2015
Event1st International Conference on Computer Science and Computational Intelligence, ICCSCI 2015 - Jakarta, Indonesia
Duration: 24 Aug 201526 Aug 2015

Keywords

  • Batik
  • Hough transform
  • motif
  • SIFT
  • voting

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