Improved Image Classification using Topological Persistence

dc.contributor.authorDey, Tamal Krishnaen_US
dc.contributor.authorMandal, Sayanen_US
dc.contributor.authorVarcho, Williamen_US
dc.contributor.editorMatthias Hullin and Reinhard Klein and Thomas Schultz and Angela Yaoen_US
dc.date.accessioned2017-09-25T06:55:48Z
dc.date.available2017-09-25T06:55:48Z
dc.date.issued2017
dc.description.abstractImage 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.sectionheadersImage Processing
dc.description.seriesinformationVision, Modeling & Visualization
dc.identifier.doi10.2312/vmv.20171272
dc.identifier.isbn978-3-03868-049-9
dc.identifier.pages161-168
dc.identifier.urihttps://doi.org/10.2312/vmv.20171272
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20171272
dc.publisherThe Eurographics Associationen_US
dc.titleImproved Image Classification using Topological Persistenceen_US
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