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An Unique Dataset for Christian Sacral Objects Identification

Authors: Marie Feslova; Michal Konopa; Katerina Hornickova; Jiri Jelinek; Radka Bunesova; Eliska Pisova; Jan Fesl;

An Unique Dataset for Christian Sacral Objects Identification

Abstract

Christian religious monuments, such 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, and there are many photographs on the Internet that can be used to build machine learning models to identify them. For little known buildings such as small churches in the Czech-German border region, the number of photographs is already significantly lower and similar approaches cannot be used for identification. Based on these facts, our team has compiled an unique dataset for the identification of the most important elements of Christian sacral buildings such 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 for identifying objects in sacred objects or the objects themselves. This dataset was created within the framework of the project Information system for medieval monuments in the Czech-Bavarian border area, No. 335, which is co-financed by the European Regional Development Fund and the state budget of the Czech Republic (Cross-border Cooperation Programme Czech Republic - Free State of Bavaria Objective ECA 2014-20).

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Keywords

Machine Learning, Identification, Christian Sacral Objects, Image Processing, Middle Age

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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