  Computational modeling of artwork meaning is complex and difficult. This is
because art interpretation is multidimensional and highly subjective. This
paper experimentally investigated the degree to which a state-of-the-art Deep
Convolutional Neural Network (DCNN), a popular Machine Learning approach, can
correctly distinguish modern conceptual art work into the galleries devised by
art curators. Two hypotheses were proposed to state that the DCNN model uses
Exhibited Properties for classification, like shape and color, but not
Non-Exhibited Properties, such as historical context and artist intention. The
two hypotheses were experimentally validated using a methodology designed for
this purpose. VGG-11 DCNN pre-trained on ImageNet dataset and discriminatively
fine-tuned was trained on handcrafted datasets designed from real-world
conceptual photography galleries. Experimental results supported the two
hypotheses showing that the DCNN model ignores Non-Exhibited Properties and
uses only Exhibited Properties for artwork classification. This work points to
current DCNN limitations, which should be addressed by future DNN models.
