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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Notes
- 1.
The DaimlerChrysler dataset is available at http://www.science.uva.nl/research/isla/dc-ped-class-benchmark.html.
- 2.
Available at http://www.genome.jp/dbget/aaindex.html.
- 3.
HeLa dataset is available at at http://murphylab.web.cmu.edu/.
- 4.
The matlab code for extracting the distance matrix is available at http://bias.csr.unibo.it/nanni/DM.zip.
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Acknowledgments
The authors would like to thank T. Ojala, M. Pietikäinen and T. Mäenpää for sharing their LBP code and V. Ojansivu and J. Heikkilä for sharing their LPQ code
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Nanni, L., Brahnam, S., Lumini, A., Barrier, T. (2014). Ensemble of Local Phase Quantization Variants with Ternary Encoding. In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_8
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