13 April 2018 Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960X (2018) https://doi.org/10.1117/12.2309553
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
This work proposes the use of Singular Spectrum Analysis (SSA) for the classification of texture images, more specifically, to enhance the performance of the Bouligand-Minkowski fractal descriptors in this task. Fractal descriptors are known to be a powerful approach to model and particularly identify complex patterns in natural images. Nevertheless, the multiscale analysis involved in those descriptors makes them highly correlated. Although other attempts to address this point was proposed in the literature, none of them investigated the relation between the fractal correlation and the well-established analysis employed in time series. And SSA is one of the most powerful techniques for this purpose. The proposed method was employed for the classification of benchmark texture images and the results were compared with other state-of-the-art classifiers, confirming the potential of this analysis in image classification.
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João Batista Florindo, João Batista Florindo, } "Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960X (13 April 2018); doi: 10.1117/12.2309553; https://doi.org/10.1117/12.2309553
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