A Data Set and a Convolutional Model for Iconography Classification in
Paintings
- URL: http://arxiv.org/abs/2010.11697v3
- Date: Mon, 26 Jul 2021 12:27:36 GMT
- Title: A Data Set and a Convolutional Model for Iconography Classification in
Paintings
- Authors: Federico Milani and Piero Fraternali
- Abstract summary: Iconography in art is the discipline that studies the visual content of artworks to determine their motifs and themes.
Applying Computer Vision techniques to the analysis of art images at an unprecedented scale may support iconography research and education.
- Score: 3.4138918206057265
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Iconography in art is the discipline that studies the visual content of
artworks to determine their motifs and themes andto characterize the way these
are represented. It is a subject of active research for a variety of purposes,
including the interpretation of meaning, the investigation of the origin and
diffusion in time and space of representations, and the study of influences
across artists and art works. With the proliferation of digital archives of art
images, the possibility arises of applying Computer Vision techniques to the
analysis of art images at an unprecedented scale, which may support iconography
research and education. In this paper we introduce a novel paintings data set
for iconography classification and present the quantitativeand qualitative
results of applying a Convolutional Neural Network (CNN) classifier to the
recognition of the iconography of artworks. The proposed classifier achieves
good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73%
Average Precision) in the task of identifying saints in Christian religious
paintings, a task made difficult by the presence of classes with very similar
visual features. Qualitative analysis of the results shows that the CNN focuses
on the traditional iconic motifs that characterize the representation of each
saint and exploits such hints to attain correct identification. The ultimate
goal of our work is to enable the automatic extraction, decomposition, and
comparison of iconography elements to support iconographic studies and
automatic art work annotation.
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