  We introduce II-20 (Image Insight 2020), a multimedia analytics approach for
analytic categorization of image collections. Advanced visualizations for image
collections exist, but they need tight integration with a machine model to
support analytic categorization. Directly employing computer vision and
interactive learning techniques gravitates towards search. Analytic
categorization, however, is not machine classification (the difference between
the two is called the pragmatic gap): a human adds/redefines/deletes categories
of relevance on the fly to build insight, whereas the machine classifier is
rigid and non-adaptive. Analytic categorization that brings the user to insight
requires a flexible machine model that allows dynamic sliding on the
exploration-search axis, as well as semantic interactions. II-20 brings 3 major
contributions to multimedia analytics on image collections and towards closing
the pragmatic gap. Firstly, a machine model that closely follows the user's
interactions and dynamically models her categories of relevance. II-20's model,
in addition to matching and exceeding the state of the art w. r. t. relevance,
allows the user to dynamically slide on the exploration-search axis without
additional input from her side. Secondly, the dynamic, 1-image-at-a-time Tetris
metaphor that synergizes with the model. It allows the model to analyze the
collection by itself with minimal interaction from the user and complements the
classic grid metaphor. Thirdly, the fast-forward interaction, allowing the user
to harness the model to quickly expand ("fast-forward") the categories of
relevance, expands the multimedia analytics semantic interaction dictionary.
Automated experiments show that II-20's model outperforms the state of the art
and also demonstrate Tetris's analytic quality. User studies confirm that II-20
is an intuitive, efficient, and effective multimedia analytics tool.
