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Semantic Sketch-Based Video Retrieval with Autocompletion

Tanase, Claudiu, Giangreco, Ivan, Rossetto, Luca, Schuldt, Heiko, Seddati, Omar, Dupont, Stephane, Altiok, Ozan Can and Sezgin, Metin (2016) Semantic Sketch-Based Video Retrieval with Autocompletion. In: IUI'16: 21st International Conference on Intelligent User Interfaces, 7 - 10 March, 2016, Sonoma California USA. ISBN 9781450341400

Abstract
The IMOTION system is a content-based video search engine that provides fast and intuitive known item search in large video collections. User interaction consists mainly of sketching, which the system recognizes in real-time and makes suggestions based on both visual appearance of the sketch (what does the sketch look like in terms of colors, edge distribution, etc.) and semantic content (what object is the user sketching). The latter is enabled by a predictive sketch-based UI that identies likely candidates for the sketched object via state-of-the-art sketch recognition techniques and offers on-screen completion suggestions. In this demo, we show how the sketch-based video retrieval of the IMOTION system is used in a collection of roughly 30,000 video shots. The system indexes collection data with over 30 visual features describing color, edge, motion, and semantic information. Resulting feature data is stored in ADAM, an efficient database system optimized for fast retrieval.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Content-based video retrieval; sketch interface
Subjects:Computer Science > Information retrieval
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Companion Publication of the 21st International Conference on Intelligent User Interfaces. IUI '16 Companion . Association for Computing Machinery. ISBN 9781450341400
Publisher:Association for Computing Machinery
Official URL:https://dl.acm.org/doi/10.1145/2876456.2879473
Copyright Information:Authors
ID Code:32427
Deposited On:20 Mar 2026 09:44 by Luca Rossetto . Last Modified 20 Mar 2026 09:44
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