Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic Modeling and Knowledge Representation in Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sweetening Ontologies with DOLCE
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Handbook on Ontologies (International Handbooks on Information Systems)
Handbook on Ontologies (International Handbooks on Information Systems)
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Modeling linguistic facets of multimedia content for semantic annotation
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
A multilingual/multimedia lexicon model for ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Modeling linguistic facets of multimedia content for semantic annotation
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
A system for the semantic multimodal analysis of news audio-visual content
EURASIP Journal on Advances in Signal Processing
An approach for grounding ontologies in raw data using foundational ontology
Information Systems
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In this paper, an ontology-driven approach for the semantic analysis of video is proposed. This approach builds on an ontology infrastructure and in particular a multimedia ontology that is based on the notions of Visual Information Object (VIO) and Multimedia Information Object (MMIO). The latter constitute extensions of the Information Object (IO) design pattern, previously proposed for refining and extending the DOLCE core ontology. This multimedia ontology, along with the more domain-specific parts of the developed knowledge infrastructure, supports the analysis of video material, models the content layer of video, and defines generic as well as domain-specific concepts whose detection is important for the analysis and description of video of the specified domain. The signal-level video processing that is necessary for linking the developed ontology infrastructure with the signal domain includes the combined use of a temporal and a spatial segmentation algorithm, a layered structure of Support Vector Machines (SVMs)-based classifiers and a classifier fusion mechanism. A Genetic Algorithm (GA) is introduced for optimizing the performed information fusion step. These processing methods support the decomposition of visual information, as specified by the multimedia ontology, and the detection of the defined domain-specific concepts that each piece of video signal, treated as a VIO, is related to. Experimental results in the domain of disaster news video demonstrate the efficiency of the proposed approach.