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arXiv · 1908.00281

Featuring the topology with the unsupervised machine learning

Kenji Fukushima·Shotaro Shiba Funai·Hideaki Iida
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Abstract

Images of line drawings are generally composed of primitive elements. One of the most fundamental elements to characterize images is the topology; line segments belong to a category different from closed circles, and closed circles with different winding degrees are nonequivalent. We investigate images with nontrivial winding using the unsupervised machine learning. We build an autoencoder model with a combination of convolutional and fully connected neural networks. We confirm that compressed data filtered from the trained model retain more than 90% of correct information on the topology, evidencing that image clustering from the unsupervised learning features the topology.

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Kenji Fukushima, Shotaro Shiba Funai, Hideaki Iida. 2019-08-01. Featuring the topology with the unsupervised machine learning. https://arxiv.org/abs/1908.00281

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