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Texture Classification Using Shearlet Transform Energy Features

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Digital Connectivity – Social Impact (CSI 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 679))

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Abstract

This paper presents a novel approach for texture classification using Shearlet Transform. The Shearlet Transform is a recently developed tool, which have the multiscale framework which allows to efficiently encode anisotropic features in multivariate problem classes. Shearlets are a newly developed extension of wavelets that are better suited to image characterization. In addition the degree of computational complexity of many proposed texture measures are very high. In this paper, a novel texture classification method that models the adjacent shearlet subband dependences. In this paper the classification efficiencies of Minimum Distance classifier was compared with SVM classifier efficiency. For texture classification, the energy features are used to represent each shearlet subband. Comprehensive validation experiments performed on different datasets proves that this research work outperforms the current methods due to efficient multiscale directional representation of Shearlet Transform.

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Author information

Authors and Affiliations

  1. Department of ECE, Mepco Schlenk Engineering College, Sivakasi, India

    K. Gopala Krishnan & R. Abinaya

  2. PSG College of Technology, Peelamedu, Coimbatore, India

    P. T. Vanathi

Authors
  1. K. Gopala Krishnan
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  2. P. T. Vanathi
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  3. R. Abinaya
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Corresponding author

Correspondence to K. Gopala Krishnan .

Editor information

Editors and Affiliations

  1. Karpagam Academy of Higher Education, Coimbatore, India

    S. Subramanian

  2. PSG College of Technology, Coimbatore, India

    R. Nadarajan

  3. International Institute of Information Technology, Bengaluru, Karnataka, India

    Shrisha Rao

  4. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    Shina Sheen

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© 2016 Springer Nature Singapore Pte Ltd.

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Gopala Krishnan, K., Vanathi, P.T., Abinaya, R. (2016). Texture Classification Using Shearlet Transform Energy Features. In: Subramanian, S., Nadarajan, R., Rao, S., Sheen, S. (eds) Digital Connectivity – Social Impact. CSI 2016. Communications in Computer and Information Science, vol 679. Springer, Singapore. https://doi.org/10.1007/978-981-10-3274-5_1

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  • DOI: https://doi.org/10.1007/978-981-10-3274-5_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3273-8

  • Online ISBN: 978-981-10-3274-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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