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Article Citation - WoS: 7Citation - Scopus: 10A FUZZY CONCEPTUAL MODEL FOR MULTIMEDIA DATA WITH A TEXT-BASED AUTOMATIC ANNOTATION SCHEME(World Scientific Publ Co Pte Ltd, 2009) Kucuk, Dilek; Burcuozgur, N.; Yazici, Adnan; Koyuncu, MuratThe size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing the domain and thereby provides an evidence for the general applicability of the model. The model is accompanied by an automatic multimedia annotation scheme which makes use of information extraction techniques on the corresponding multimedia texts.Conference Object Citation - WoS: 3Flexible Content Extraction and Querying for Videos(Springer-verlag Berlin, 2011) Demir, Utku; Koyuncu, Murat; Yazici, Adnan; Yilmaz, Turgay; Sert, MustafaIn 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.Conference Object A Novel Fuzzy Visual Object Classification Approach(Ieee, 2012) Altintakan, Umit Lutfu; Yazici, Adnan; Koyuncu, MuratSupport Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM.Conference Object Citation - WoS: 1A Framework for Fuzzy Video Content Extraction, Storage and Retrieval(Ieee, 2010) Koyuncu, Murat; Yilmaz, Turgay; Yildirim, Yakup; Yazici, AdnanThis 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.
