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POSE-ID-on—A Novel Framework for Artwork Pose Clustering

by 1,*,† and 1,2,†
Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), Sapienza University of Rome, 00185 Rome, Italy
Sapienza School for Advanced Studies, 00161 Rome, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Susana Del Pozo and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 257;
Received: 3 February 2021 / Revised: 2 April 2021 / Accepted: 5 April 2021 / Published: 11 April 2021
(This article belongs to the Special Issue Machine Learning and Deep Learning in Cultural Heritage)
In this work, we focus our attention on the similarity among works of art based on human poses and the actions they represent, moving from the concept of Pathosformel in Aby Warburg. This form of similarity is investigated by performing a pose clustering of the human poses, which are modeled as 2D skeletons and are defined as sets of 14 points connected by limbs. To build a dataset of properly annotated artwork images (that is, including the 2D skeletons of the human figures represented), we relied on one of the most popular, recent, and accurate deep learning frameworks for pose tracking of human figures, namely OpenPose. To measure the similarity between human poses, two alternative distance functions are proposed. Moreover, we developed a modified version of the K-Medians algorithm to cluster similar poses and to find a limited number of poses that are representative of the whole dataset. The proposed approach was also compared to two popular clustering strategies, that is, K-Means and the Nearest Point Algorithm, showing higher robustness to outliers. Finally, we assessed the validity of the proposed framework, which we named POSE-ID-on, in both a qualitative and in a quantitative way by simulating a supervised setting, since we lacked a proper reference for comparison. View Full-Text
Keywords: pose clustering; pose tracking; digital humanities; image retrieval; Warburg pose clustering; pose tracking; digital humanities; image retrieval; Warburg
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MDPI and ACS Style

Marsocci, V.; Lastilla, L. POSE-ID-on—A Novel Framework for Artwork Pose Clustering. ISPRS Int. J. Geo-Inf. 2021, 10, 257.

AMA Style

Marsocci V, Lastilla L. POSE-ID-on—A Novel Framework for Artwork Pose Clustering. ISPRS International Journal of Geo-Information. 2021; 10(4):257.

Chicago/Turabian Style

Marsocci, Valerio; Lastilla, Lorenzo. 2021. "POSE-ID-on—A Novel Framework for Artwork Pose Clustering" ISPRS Int. J. Geo-Inf. 10, no. 4: 257.

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