Recently, the use of neural networks for image classification has become widely spread. Thanks to the availability of increased computational power, better performing architectures have been designed, such as the Deep Neural networks. In this work, we propose a novel image representation framework exploiting the Deep p- Fibonacci scattering network. The architecture is based on the structured p-Fibonacci scattering over graph data. This approach allows to provide good accuracy in classification while reducing the computational complexity. Experimental results demonstrate that the performance of the proposed method is comparable to state-of-the-art unsupervised methods while being computationally more efficient.

Battisti, F., Carli, M., De Paola, E., Egiazarian, K. (2018). Deep p-Fibonacci scattering networks. In IS and T International Symposium on Electronic Imaging Science and Technology (pp.193-1-193-5). Society for Imaging Science and Technology [10.2352/ISSN.2470-1173.2018.13.IPAS-193].

Deep p-Fibonacci scattering networks

Battisti F.;Carli M.;
2018

Abstract

Recently, the use of neural networks for image classification has become widely spread. Thanks to the availability of increased computational power, better performing architectures have been designed, such as the Deep Neural networks. In this work, we propose a novel image representation framework exploiting the Deep p- Fibonacci scattering network. The architecture is based on the structured p-Fibonacci scattering over graph data. This approach allows to provide good accuracy in classification while reducing the computational complexity. Experimental results demonstrate that the performance of the proposed method is comparable to state-of-the-art unsupervised methods while being computationally more efficient.
Battisti, F., Carli, M., De Paola, E., Egiazarian, K. (2018). Deep p-Fibonacci scattering networks. In IS and T International Symposium on Electronic Imaging Science and Technology (pp.193-1-193-5). Society for Imaging Science and Technology [10.2352/ISSN.2470-1173.2018.13.IPAS-193].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/364044
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