• Deep Learning Based Attribute Representation in Ancient Vase Paintings

    Author(s):
    Torsten Bendschus, Prathmesh Madhu (see profile)
    Date:
    2020
    Group(s):
    DH2020
    Subject(s):
    Classical archaeology, Computer vision, Greek, Machine learning, Narrative
    Item Type:
    Conference proceeding
    Conf. Title:
    DH2020
    Conf. Org.:
    ADHO
    Conf. Loc.:
    Ottawa, Canada
    Conf. Date:
    22-24 July
    Tag(s):
    deep learning, vases
    Permanent URL:
    http://dx.doi.org/10.17613/03b6-zv04
    Abstract:
    The understanding of iconography and visual narration in ancient imagery is one of the main foci in the field of Classical Archaeology, e.g. in Attic vase paintings of the fifth century B.C. In order to depict the situations and actions of a narrative as well as to characterise its protagonists, ancient Greek artists made use of a broad variety of often similar image elements [1]. The interaction and meaningful relationship of the protagonists is depicted with significant postures and gestures (schemata) in order to illustrate key aspects of the storyline [2, 3]. These schemes are not restricted to a certain iconography, so that visual links between different images occur. Being familiar with these relationships the ancient viewer could detect the specific narration and understand the meaning of the image. For example, the scheme of leading the bride in Attic vase paintings is characterised by a significant leading-gesture (χεῖρ’ ἐπὶ καρπῷ – hand on wrist / hand on hand) that relates bride and bridegroom in non-mythological wedding scenes, thereby expressing a hierarchy of the two figures in active or passive parts respectively. Both protagonists are connected in a communicative way defined by meaningful postures and gestures.
    Metadata:
    Status:
    Published
    Last Updated:
    4 months ago
    License:
    All Rights Reserved
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