II-20: Intelligent and pragmatic analytic categorization of image collections
| Autor: | Marcel Worring, Jarke J. van Wijk, Jan Zahálka |
|---|---|
| Přispěvatelé: | Visualization |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Visual analytics Computer science pragmatic gap Computer Science - Information Retrieval Interactive Learning Data visualization Information retrieval business.industry image data analytic categorization Computer Graphics and Computer-Aided Design Multimedia (cs.MM) Categorization Multimedia Analytics Signal Processing Task analysis Computer Vision and Pattern Recognition User interface business Classifier (UML) Computer Science - Multimedia Information Retrieval (cs.IR) Software Multimedia analytics |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics, 27(2):9230430, 422-431. IEEE Computer Society arXiv, 2020:2005.02149. Cornell University Library |
| ISSN: | 1077-2626 2331-8422 |
| DOI: | 10.1109/TVCG.2020.3030383 |
| Popis: | 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. Comment: 9 pages, 7 figures, 1 table. Camera-ready paper, to appear in IEEE VIS 2020 and IEEE TVCG in January 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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