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.
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Acknowledgments
This work is supported by the research grant from TUBITAK with the grant number 114R0182. We also thank to all of the previous researchers of Multimedia Db. Lab. at METU who have contributed to this study.
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Yazici, A., Sattari, S., Yilmaz, T., Sert, M., Koyuncu, M., Gulen, E. (2016). METU-MMDS: An Intelligent Multimedia Database System for Multimodal Content Extraction and Querying. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_33
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DOI: https://doi.org/10.1007/978-3-319-27674-8_33
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