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Matrix based cyclic spectral estimator for fast and robust texture classification

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Abstract

Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency Smoothing (DFS). Further, for comparable efficacy of the cyclostationary based analyzers, two new algorithms for implementation of both SSCA and FAM are proposed. The algorithms are fast, parallel, and linear-algebraic based, which brings many advantages in computational competence, feature generation flexibility, simplicity, and hardware implementation. SSCA as the unused promising texture analyzer and the new FAM implementation are compared with other state of the art methods in the case of classification accuracy, noise resistance and feature efficiency.

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Authors and Affiliations

  1. Department of Electrical Engineering, Urmia University, Urmia, Iran

    Sadid Sahami & Mehdi Chehel Amirani

  2. Urmia University, Nazloo, P. Box: 15311-57561, Urmia, Iran

    Mehdi Chehel Amirani

Authors
  1. Sadid Sahami
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  2. Mehdi Chehel Amirani
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Correspondence to Sadid Sahami.

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Sahami, S., Chehel Amirani, M. Matrix based cyclic spectral estimator for fast and robust texture classification. Vis Comput 29, 1245–1257 (2013). https://doi.org/10.1007/s00371-012-0766-0

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