Implicit Human-Centered Tagging

TL;DR: The Implicit Human-Centered Tagging (IHCT) method as discussed by the authors ) is a technique for automatic extraction of tags from nonverbal behavioral feedback of media users, where nonverbal behaviors displayed when interacting with multimedia data (e.g., facial expressions, head nods, etc.) provide information useful for improving the tag sets associated with the data.

Abstract: This paper provides a general introduction to the concept of Implicit Human-Centered Tagging (IHCT) — the automatic extraction of tags from nonverbal behavioral feedback of media users. The main idea behind IHCT is that nonverbal behaviors displayed when interacting with multimedia data (e.g., facial expressions, head nods, etc.) provide information useful for improving the tag sets associated with the data. As such behaviors are displayed naturally and spontaneously, no effort is required from the users, and this is why the resulting tagging process is said to be “implicit”. Tags obtained through IHCT are expected to be more robust than tags associated with the data explicitly, at least in terms of: generality (they make sense to everybody) and statistical reliability (all tags will be sufficiently represented). The paper discusses these issues in detail and provides an overview of pioneering efforts in the field.