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Deep p-Fibonacci scattering networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationElectronic Imaging
Subtitle of host publicationImage Processing: Algorithms and Systems XVI
PublisherSociety for Imaging Science and Technology
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventIS&T International Symposium on Electronic Imaging -
Duration: 28 Jan 20182 Feb 2018

Publication series

Name
ISSN (Electronic)2470-1173

Conference

ConferenceIS&T International Symposium on Electronic Imaging
Period28/01/182/02/18

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.