TY - GEN
T1 - Towards a Hierarchical Multitask Classification Framework for Cultural Heritage
AU - Belhi, Abdelhak
AU - Bouras, Abdelaziz
AU - Foufou, Sebti
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image classification. In this paper, we present a novel approach of hierarchical classification for cultural heritage assets. The metadata structural differences that exist between cultural assets motivated us to design a classification framework that can efficiently perform the classification of multiple types of assets. Our approach relies on several deep learning classifiers, each of them is assigned the task of classifying a certain type of assets. The classification framework starts the labeling process by first determining the asset type. The asset is then assigned to a specific classifier in order to be annotated with data fields related to its type. As a preliminary step, we successfully designed a general cultural type classifier and a specific type classifier for paintings. Our approach is currently achieving interesting results and is set to be improved by the integration of more asset types.
AB - Digital technologies such as 3D imaging, data analytics and computer vision opened the door to a large set of applications in cultural heritage. Digital acquisition of a cultural assets takes nowadays a couple of seconds thanks to the achievements in 2D and 3D acquisition technologies. However, enriching these cultural assets with labels and relevant metadata is still not fully automatized especially due to their nature and specificities. With the recent publication of several cultural heritage datasets, many researchers are tackling the challenge of effectively classifying and annotating digital heritage. The challenges that are often addressed are related to visual recognition and image classification. In this paper, we present a novel approach of hierarchical classification for cultural heritage assets. The metadata structural differences that exist between cultural assets motivated us to design a classification framework that can efficiently perform the classification of multiple types of assets. Our approach relies on several deep learning classifiers, each of them is assigned the task of classifying a certain type of assets. The classification framework starts the labeling process by first determining the asset type. The asset is then assigned to a specific classifier in order to be annotated with data fields related to its type. As a preliminary step, we successfully designed a general cultural type classifier and a specific type classifier for paintings. Our approach is currently achieving interesting results and is set to be improved by the integration of more asset types.
KW - Convolutional Neural Networks
KW - Cultural heritage
KW - Digital heritage
KW - Digital preservation
KW - Multitask Classification
UR - http://www.scopus.com/inward/record.url?scp=85061933363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061933363&partnerID=8YFLogxK
U2 - 10.1109/AICCSA.2018.8612815
DO - 10.1109/AICCSA.2018.8612815
M3 - Conference contribution
AN - SCOPUS:85061933363
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018
PB - IEEE Computer Society
T2 - 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018
Y2 - 28 October 2018 through 1 November 2018
ER -