Browsing by Author "Yilmaz,T."

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Citation - Scopus: 3
Flexible Content Extraction and Querying for Videos
(2011) Demir,U.; Koyuncu,M.; Yazici,A.; Yilmaz,T.; Sert,M.
In this study, a multimedia database system which includes a semantic content extractor, a high-dimensional index structure and an intelligent fuzzy object-oriented database component is proposed. The proposed system is realized by following a component-oriented approach. It supports different flexible query capabilities for the requirements of video users, which is the main focus of this paper. The query performance of the system (including automatic semantic content extraction) is tested and analyzed in terms of speed and accuracy. © 2011 Springer-Verlag.
Citation - Scopus: 1
A Framework for Fuzzy Video Content Extraction, Storage and Retrieval
(2010) Koyuncu,M.; Yilmaz,T.; Yildirim,Y.; Yazici,A.
This study presents a new comprehensive framework for semantic content extraction from raw video, storage of the extracted data and retrieval of the stored data. Objects, spatial relations between objects, events and temporal relations between events, which are considered as semantic contents of the video, are extracted automatically to a certain extend with the developed approach. Extraction process is supported by manual annotation when automatic extraction is not satisfactory. The extracted information is stored in an intelligent fuzzy object-oriented database in which the database is enhanced with a fuzzy knowledge-based system. Domain specific deduction rules can be defined to derive new information about semantic contents of the video. The database is also supported by an access structure to increase retrieval efficiency. The proposed framework is capable of handling uncertain data arising from annotation process or video nature. © 2010 IEEE.
Citation - Scopus: 1
Metu-Mmds: an Intelligent Multimedia Database System for Multimodal Content Extraction and Querying
(Springer Verlag, 2016) Yazici,A.; Sattari,S.; Yilmaz,T.; Sert,M.; Koyuncu,M.; Gulen,E.
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.