TY - GEN
T1 - METU-MMDS
T2 - 22nd International Conference on MultiMedia Modeling, MMM 2016
AU - Yazici, Adnan
AU - Sattari, Saeid
AU - Yilmaz, Turgay
AU - Sert, Mustafa
AU - Koyuncu, Murat
AU - Gulen, Elvan
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84955258028&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84955258028&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27674-8_33
DO - 10.1007/978-3-319-27674-8_33
M3 - Conference contribution
AN - SCOPUS:84955258028
SN - 9783319276731
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 354
EP - 360
BT - MultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings
A2 - Hong, Richang
A2 - Sebe, Nicu
A2 - Tian, Qi
A2 - Qi, Guo-Jun
A2 - Huet, Benoit
A2 - Liu, Xueliang
PB - Springer Verlag
Y2 - 4 January 2016 through 6 January 2016
ER -