METU MMDS An Intelligent Multimedia Database System for Multimodal Content Extraction and Querying

Date

2016-01-04

Author

Yazıcı, Adnan
Yılmaz, Turgay
Gulen, Elvan
Koyuncu, Murat
Sert, Mustafa

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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.

Subject Keywords

Named entity recognition, Natural language processing (NLP), Conditional random fields

URI

https://hdl.handle.net/11511/73169

DOI

https://doi.org/10.1007/978-3-319-27674-8_33

Conference Name

22nd International Conference on MultiMedia Modeling, MMM 2016 (4 January 2016 through 6 January 2016)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Yazıcı, T. Yılmaz, E. Gulen, M. Koyuncu, and M. Sert, “METU MMDS An Intelligent Multimedia Database System for Multimodal Content Extraction and Querying,” Miami; United States, 2016, vol. 9517, p. 354, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73169.