Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Semantic Indexing of Multimedia Documents
IEEE MultiMedia
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Semantic annotation of soccer videos: automatic highlights identification
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Semi-automatic, data-driven construction of multimedia ontologies
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Modal keywords, ontologies, and reasoning for video understanding
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Dynamic pictorial ontologies for video digital libraries annotation
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Building a comprehensive ontology to refine video concept detection
Proceedings of the international workshop on Workshop on multimedia information retrieval
Semantic representation of multimedia content: Knowledge representation and semantic indexing
Multimedia Tools and Applications
Towards a structure-based multimedia retrieval model
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Foundations and Trends in Information Retrieval
Using textual and structural context for searching Multimedia Elements
International Journal of Business Intelligence and Data Mining
Narrative theme navigation for sitcoms supported by fan-generated scripts
Multimedia Tools and Applications
A spatio-temporal pyramid matching for video retrieval
Computer Vision and Image Understanding
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Classifying video elements according to some pre-defined ontology of the video content domain is a typical way to perform video annotation. Ontologies are defined by establishing relationships between linguistic terms that specify domain concepts at different abstraction levels. However, although linguistic terms are appropriate to distinguish event and object categories, they are inadequate when they must describe specific patterns of events or video entities. Instead, in these cases, pattern specifications can be better expressed through visual prototypes that capture the essence of the event or entity. Therefore pictorially enriched ontologies, that include both visual and linguistic concepts, can be useful to support video annotation up to the level of detail of pattern specification.This paper presents pictorially enriched ontologies and discusses a solution for their implementation for the soccer video domain. An unsupervised clustering method is proposed in order to create the enriched ontologies by defining visual prototypes representing specific patterns of highlights and adding them as visual concepts to the ontology.An algorithm that uses pictorially enriched ontologies to perform automatic soccer video annotation is proposed and results for typical highlights are presented. Annotation is performed associating occurrences of events, or entities, to higher level concepts by checking their proximity to visual concepts that are hierarchically linked to higher level semantics.