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Classification of Batik Kain Besurek Image Using Speed Up Robust Features (SURF) and Gray Level Co-occurrence Matrix (GLCM)

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Soft Computing in Data Science (SCDS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 788))

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Abstract

Indonesian Batik has been endorsed as world cultural heritages by UNESCO. The batik consists of various motifs each of whom represents characteristics of each Indonesian province. One of the motifs is called as Batik Kain Besurek or shortly Batik Besurek, originally from Bengkulu Province. This motif constitutes a motif family consisting of five main motifs: Kaligrafi, Rafflesia, Relung Paku, Rembulan, and Burung Kuau. Currently most Batik Besureks reflect a creation developed from combination of main motifs so that it is not easy to identify its main motif. This research aims to classify Indonesian batik according to its image into either batik besurek or not batik besurek as well as reidentifying its more detailed motif for the identified batik besurek. The classification is approached through six classes: five classes in accordance with classification of Batik Besurek and a class of not Batik Besurek. The preprocessing system converts images to grayscale and followed by resizing. The feature extraction uses GLCM method yielding six features and SURF method yielding 64 descriptors. The extraction results are combined by assigning weight on both methods in which the weighting scheme is tested. Moreover, the image classification uses a method of k-Nearest Neighbor. The system is tested through some scenarios for the feature extraction and some values k in k-NN to classify the main motif of Batik Besurek. So far the result can improve system performance with an accuracy of 95.47% according to weighting 0.1 and 0.9 for GLCM and SURF respectively, and k = 3.

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Authors and Affiliations

  1. Department Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia

    Fathin Ulfah Karimah & Agus Harjoko

Authors
  1. Fathin Ulfah Karimah
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  2. Agus Harjoko
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Correspondence to Agus Harjoko .

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Editors and Affiliations

  1. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  2. University of Tennessee at Knoxville, Knoxville, Tennessee, USA

    Michael W. Berry

  3. Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

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Karimah, F.U., Harjoko, A. (2017). Classification of Batik Kain Besurek Image Using Speed Up Robust Features (SURF) and Gray Level Co-occurrence Matrix (GLCM). In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_7

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  • DOI: https://doi.org/10.1007/978-981-10-7242-0_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7241-3

  • Online ISBN: 978-981-10-7242-0

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