Savanta--search, analysis, visualisation and navigation of temporal annotations
Multimedia Tools and Applications
A Fuzzy Rule-Based System with Ontology for Summarization of Multi-camera Event Sequences
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Learning ontology rules for semantic video annotation
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Parallel neural networks for multimodal video genre classification
Multimedia Tools and Applications
Characterizing Multimedia Objects through Multimodal Content Analysis and Fuzzy Fingerprints
Advanced Internet Based Systems and Applications
A knowledge synthesizing approach for classification of visual information
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Visual video retrieval system using MPEG-7 descriptors
Proceedings of the Third International Conference on SImilarity Search and APplications
Video indexing and retrieval in compressed domain using fuzzy-categorization
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A case study on logging visual activities: chess game
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
SHIATSU: tagging and retrieving videos without worries
Multimedia Tools and Applications
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A generic system for automatic annotation of videos is introduced. The proposed approach is based on the premise that the rules needed to infer a set of high-level concepts from low-level descriptors cannot be defined a priori. Rather, knowledge embedded in the database and interaction with an expert user is exploited to enable system learning. Underpinning the system at the implementation level is preannotated data that dynamically creates signification links between a set of low-level features extracted directly from the video dataset and high-level semantic concepts defined in the lexicon. The lexicon may consist of words, icons, or any set of symbols that convey the meaning to the user. Thus, the lexicon is contingent on the user, application, time, and the entire context of the annotation process. The main system modules use fuzzy logic and rule mining techniques to approximate human-like reasoning. A rule-knowledge base is created on a small sample selected by the expert user during the learning phase. Using this rule-knowledge base, the system automatically assigns keywords from the lexicon to nonannotated video clips in the database. Using common low-level video representations, the system performance was assessed on a database containing hundreds of broadcasting videos. The experimental evaluation showed robust and high annotation accuracy. The system architecture offers straightforward expansion to relevance feedback and autonomous learning capabilities.