  Research on mid-level image representations has conventionally concentrated
relatively obvious attributes and overlooked non-obvious attributes, i.e.,
characteristics that are not readily observable when images are viewed
independently of their context or function. Non-obvious attributes are not
necessarily easily nameable, but nonetheless they play a systematic role in
people`s interpretation of images. Clusters of related non-obvious attributes,
called interpretation dimensions, emerge when people are asked to compare
images, and provide important insight on aspects of social images that are
considered relevant. In contrast to aesthetic or affective approaches to image
analysis, non-obvious attributes are not related to the personal perspective of
the viewer. Instead, they encode a conventional understanding of the world,
which is tacit, rather than explicitly expressed. This paper introduces a
procedure for discovering non-obvious attributes using crowdsourcing. We
discuss this procedure using a concrete example of a crowdsourcing task on
Amazon Mechanical Turk carried out in the domain of fashion. An analysis
comparing discovered non-obvious attributes with user tags demonstrated the
added value delivered by our procedure.
