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Deep convolutional embedding for digitized painting clustering
[article]
2020
arXiv
pre-print
Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely difficult. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, we propose to use a deep convolutional embedding model for digitized painting clustering, in which the task of mapping the raw input data to an
arXiv:2003.08597v2
fatcat:mhxomrao65g5vmeuwx3ehp3lsu