Deep p-Fibonacci scattering networks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Electronic Imaging |
| Subtitle of host publication | Image Processing: Algorithms and Systems XVI |
| Publisher | Society for Imaging Science and Technology |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A4 Article in a conference publication |
| Event | IS&T International Symposium on Electronic Imaging - Duration: 28 Jan 2018 → 2 Feb 2018 |
Publication series
| Name | |
|---|---|
| ISSN (Electronic) | 2470-1173 |
Conference
| Conference | IS&T International Symposium on Electronic Imaging |
|---|---|
| Period | 28/01/18 → 2/02/18 |
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.