Improved Image Classification using Topological Persistence
| dc.contributor.author | Dey, Tamal Krishna | en_US |
| dc.contributor.author | Mandal, Sayan | en_US |
| dc.contributor.author | Varcho, William | en_US |
| dc.contributor.editor | Matthias Hullin and Reinhard Klein and Thomas Schultz and Angela Yao | en_US |
| dc.date.accessioned | 2017-09-25T06:55:48Z | |
| dc.date.available | 2017-09-25T06:55:48Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Image classification has been a topic of interest for many years. With the advent of Deep Learning, impressive progress has been made on the task, resulting in quite accurate classification. Our work focuses on improving modern image classification techniques by considering topological features as well. We show that incorporating this information allows our models to improve the accuracy, precision and recall on test data, thus providing evidence that topological signatures can be leveraged for enhancing some of the state-of-the art applications in computer vision. | en_US |
| dc.description.sectionheaders | Image Processing | |
| dc.description.seriesinformation | Vision, Modeling & Visualization | |
| dc.identifier.doi | 10.2312/vmv.20171272 | |
| dc.identifier.isbn | 978-3-03868-049-9 | |
| dc.identifier.pages | 161-168 | |
| dc.identifier.uri | https://doi.org/10.2312/vmv.20171272 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vmv20171272 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Improved Image Classification using Topological Persistence | en_US |