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Feature Extraction for Image Content Retrieval in Thai Traditional Painting with SIFT Algorithms

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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

This research presents a novel algorithm for feature extraction in Thai traditional painting by using keypoint generated from SIFT algorithms. The proposed algorithms aim to retrieve knowledge inside an image. Content Based Image Retrieval (CBIR) technique was applied. The generated keypoint and descriptors were used as input in neural network for training. Neural network technique was used for image classification to get details of image. The algorithms were tested with the corridor around the temple of the Emerald Buddha. The experimental results show that the proposed framework can efficiently give the correct descriptions to the image compared to using the traditional method. The proposed algorithms can be used to classify the type of Thai traditional painting which can provide the accuracy about 70–80% in average.

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Acknowledgment

This research was funded by King Mongkut’s University of Technology North Bangkok. Contract no. KMUTNB-60-GEN-016.

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Authors and Affiliations

  1. Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, Bangkok, 10800, Thailand

    Sathit Prasomphan

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  1. Sathit Prasomphan
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Correspondence to Sathit Prasomphan .

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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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Prasomphan, S. (2017). Feature Extraction for Image Content Retrieval in Thai Traditional Painting with SIFT Algorithms. 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_5

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  • DOI: https://doi.org/10.1007/978-981-10-7242-0_5

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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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