This paper presents a preliminary investigation aimed at developing a tool for visual link retrieval and knowledge discovery in painting datasets. The proposed framework is based on a deep convolutional network to perform feature extraction and on a fully-unsupervised nearest neighbor approach to retrieve visual links among digitized paintings. Moreover, the proposed method makes it possible to study influences among artists by means of graph analysis. The tool is intended to help art historians better understand visual arts.
Towards a Tool for Visual Link Retrieval and Knowledge Discovery in Painting Datasets
Castellano, Giovanna;Vessio, Gennaro
2020-01-01
Abstract
This paper presents a preliminary investigation aimed at developing a tool for visual link retrieval and knowledge discovery in painting datasets. The proposed framework is based on a deep convolutional network to perform feature extraction and on a fully-unsupervised nearest neighbor approach to retrieve visual links among digitized paintings. Moreover, the proposed method makes it possible to study influences among artists by means of graph analysis. The tool is intended to help art historians better understand visual arts.File in questo prodotto:
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