Ensemble of Local Phase Quantization Variants with Ternary Encoding

  • Loris Nanni
  • Sheryl Brahnam
  • Alessandra Lumini
  • Tonya Barrier
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 506)

Abstract

In this chapter, we present some variants of local phase quantization (LPQ), a novel texture descriptor that has been shown to perform well on a variety of classification tasks. After providing an extensive review of LPQ, we report experiments using several new LPQ derivatives obtained by varying LPQ parameters and by using a ternary rather than the binary encoding scheme. Multiple parameter sets are generated and each set is used to train a standard machine-learning classifier, a stand-alone support vector machine. The ensemble is then combined using the sum rule. Extensive experiments are conducted using six different datasets. Our method is compared along with the best state-of-the-art methods for solving each problem represented by the datasets. In each case, the best result is obtained using an ensemble with LPQ variants and ternary encoding. In this study, we also examine the distribution in the images of the most important bins of the LPQ histograms using Gabor filters. We find that incorporating this information into our best texture descriptor approach produces even better results.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Loris Nanni
    • 1
  • Sheryl Brahnam
    • 3
  • Alessandra Lumini
    • 2
  • Tonya Barrier
    • 3
  1. 1.Department of Information EngineeringUniversity of PaduaPadovaItaly
  2. 2.Department of Computer Science and Engineering (DISI)Universit à di BolognaCesenaItaly
  3. 3.Computer Information SystemsMissouri State UniversitySpringfieldUSA

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