  Cultural heritage is the asset of all the peoples of the world. The
preservation and inheritance of cultural heritage is conducive to the progress
of human civilization. In northwestern China, there is a world heritage site --
Mogao Grottoes -- that has a plenty of mural paintings showing the historical
cultures of ancient China. To study these historical cultures, one critical
procedure is to date the mural paintings, i.e., determining the era when they
were created. Until now, most mural paintings at Mogao Grottoes have been dated
by directly referring to the mural texts or historical documents. However, some
are still left with creation-era undetermined due to the lack of reference
materials. Considering that the drawing style of mural paintings was changing
along the history and the drawing style can be learned and quantified through
painting data, we formulate the problem of mural-painting dating into a problem
of drawing-style classification. In fact, drawing styles can be expressed not
only in color or curvature, but also in some unknown forms -- the forms that
have not been observed. To this end, besides sophisticated color and shape
descriptors, a deep convolution neural network is designed to encode the
implicit drawing styles. 3860 mural paintings collected from 194 different
grottoes with determined creation-era labels are used to train the
classification model and build the dating method. In experiments, the proposed
dating method is applied to seven mural paintings which were previously dated
with controversies, and the exciting new dating results are approved by the
Dunhuang expert.
