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Performance analysis of wave atom transform in texture classification

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Abstract

Texture classification is an important application in image processing and pattern recognition such as detection of defects on the materials and diseases from the medical images. This paper presents the performance of wave atom transform on texture classification. Wave atom transform is a new multi-resolution technique that not only captures the coherence of the pattern along the oscillations, but also the pattern across the oscillations. The classification is done using a wave atom–transformed features reduced by singular value decomposition and a support vector machine. Experimental results are presented to demonstrate the effectiveness of this approach on Brodatz database, Alzheimer’s Disease Neuro Imaging database for Alzheimer’s disease classification and liver computed tomography images for tumor classification. The experimental results demonstrate that the proposed approach gives a percent correct classification of 97.29 % on Brodatz database, classification accuracy of 94 % on Alzheimer’s Disease Neuro Imaging database for Alzheimer’s disease diagnosis and 93.3 % on liver computed tomography images for tumor classification.

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Authors and Affiliations

  1. Department of ECE, Noorul Islam College of Engineering, Kumaracoil, 629180, India

    J. Rajeesh & R. S. Moni

  2. Department of EIE, Noorul Islam College of Engineering, Kumaracoil, 629180, India

    S. S. Kumar

Authors
  1. J. Rajeesh
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  2. R. S. Moni
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  3. S. S. Kumar
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Correspondence to J. Rajeesh.

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Rajeesh, J., Moni, R.S. & Kumar, S.S. Performance analysis of wave atom transform in texture classification. SIViP 8, 923–930 (2014). https://doi.org/10.1007/s11760-012-0337-x

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  • DOI: https://doi.org/10.1007/s11760-012-0337-x

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