II-20: Intelligent and pragmatic analytic categorization of image collections
This addresses the pragmatic gap in image analysis for users needing adaptive categorization tools, though it appears incremental in advancing existing multimedia analytics techniques.
The paper tackles the problem of analytic categorization of image collections by introducing II-20, a multimedia analytics approach that integrates a flexible machine model with interactive visualizations to support dynamic category creation and exploration, outperforming state-of-the-art methods in automated experiments and user studies.
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.