Image clustering by autoencoders
Alexey Kovalenko, Yana Demyanenko · 2019
This paper describes an approach to solving the problem of finding similar images by visual similarity using neural networks on previously unmarked data. We propose to build special architecture of the neural network - autoencoder, through which high-level features are extracted from images. The search for the nearest elements is realized by the Euclidean metric in the generated feature space, after a preliminary decomposition into two-dimensional space. Proposed approach of generate feature space can be applied to the classification task using pre-clustering.