An unique dataset for Christian sacral objects identification
Marie Feslová,
Michal Konopa,
Kateřina Horníčková,
Jiří Jelínek,
Eliška Píšová,
Radka Bunešová,
Jan Fesl
Affiliations
Marie Feslová
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic
Michal Konopa
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic
Kateřina Horníčková
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic; University of South Bohemia, Faculty of Arts, Institute of Arts and Culture Studies, Branišovská 31a, 370 05, Czech Republic
Jiří Jelínek
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic
Eliška Píšová
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic
Radka Bunešová
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic
Jan Fesl
University of South Bohemia, Faculty of Science, Department of Informatics, Branišovská 31a, 370 05, Czech Republic; Corresponding author.
Christian religious monuments as cathedrals, chapels, and temples, are found in many places on our planet. World-famous buildings such as the Notre Dame Cathedral in Paris, Gaudi's Cathedral in Barcelona, and St. Vitus Cathedral in Prague are commonly known. Many online photographs can be used to build machine-learning models to identify them. The number of photographs is already significantly lower for little-known buildings, such as small churches in the Czech-German border region, and similar approaches cannot be used for identification. Based on these facts, our team has compiled a unique dataset for identifying the most important elements of Christian sacral buildings as altars, frescoes, pulpits, etc., which are almost always found in them. Our data set was manually created from several thousand real photographs. This dataset seems to be very usable, e.g., for creating new machine learning models and identifying objects in sacred objects or the objects themselves.