Abstract
In this paper, we present an evaluation of texture descriptors’ robustness when interpolation methods are applied over rotated images. We propose a novel rotation invariant texture descriptor called Sampled Local Mapped Pattern Magnitude (SLMP_M) and we compare it with well-known published texture descriptors. The compared descriptors are the Completed Local Binary Pattern (CLBP), and two Discrete Fourier Transform (DFT)-based methods called the Local Ternary Pattern DFT and the Improved Local Ternary Pattern DFT. Experiments were performed on the Kylberg Sintorn Rotation Dataset, a database of natural textures that were rotated using hardware and computational procedures. Five interpolation methods were investigated: Lanczos, B-spline, Cubic, Linear and Nearest Neighbor with nine directions. Experimental results show that our proposed method makes a robust texture discrimination, overcoming traditional texture descriptors and works better in different interpolations.
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Acknowledgements
The authors would like to acknowledge the Sao Paulo Research Foundation (FAPESP) (Grant Process #2015/20812-5) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for the financial support.
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Vieira, R.T., Negri, T.T., Gonzaga, A. (2016). Robustness of Rotation Invariant Descriptors for Texture Classification. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_25
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DOI: https://doi.org/10.1007/978-3-319-50835-1_25
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