Metu-Mmds: an Intelligent Multimedia Database System for Multimodal Content Extraction and Querying
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Date
2016
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Springer Verlag
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Abstract
Managing a large volume of multimedia data, which contain various modalities (visual, audio, and text), reveals the need for a specialized multimedia database system (MMDS) to efficiently model, process, store and retrieve video shots based on their semantic content. This demo introduces METU-MMDS, an intelligent MMDS which employs both machine learning and database techniques. The system extracts semantic content automatically by using visual, audio and textual data, stores the extracted content in an appropriate format and uses this content to efficiently retrieve video shots. The system architecture supports various multimedia query types including unimodal querying, multimodal querying, query-by-concept, query-by-example, and utilizes a multimedia index structure for efficiently querying multi-dimensional multimedia data. We demonstrate METU-MMDS for semantic data extraction from videos and complex multimedia querying by considering content and concept-based queries containing all modalities. © Springer International Publishing Switzerland 2016.
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FX Palo Alto Laboratory; Springer Publishing; University of Central Florida
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 22nd International Conference on MultiMedia Modeling, MMM 2016 -- 4 January 2016 through 6 January 2016 -- Miami -- 160449
Volume
9517
Issue
Start Page
354
End Page
360