MAGAD-BFS: A learning method for Beta fuzzy systems based on a multi-agent genetic algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multimedia semantic indexing using model vectors
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A reranking approach for context-based concept fusion in video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Refining video annotation by exploiting pairwise concurrent relation
Proceedings of the 15th international conference on Multimedia
Exploring inter-concept relationship with context space for semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Semantic browsing in large scale videos collection
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Multimedia indexing systems based on semantic concept detectors are incomplete in the semantic sense. We can improve the effectiveness of these systems by using knowledge-based approaches which utilize semantic knowledge. In this paper, we propose a novel and efficient approach to enhance semantic concept detection in multimedia content, by exploiting contextual information about concepts from visual modality. First, a semantic knowledge is extracted via a contextual annotation framework. Second, a Fuzzy ontology is proposed to represent the fuzzy relationships (roles and rules) among every context and its semantic concepts. We use an abduction engine based on βeta function as a membership function for fuzzy rules. Third, a deduction engine is used to handle richer results in our video indexing system by running the proposed fuzzy ontology. Experiments on TRECVID 2010 benchmark have been performed to evaluate the performance of this approach. The obtained results show consistent improvement in semantic concepts detection, when a context space is used, and a good degree of indexing effectiveness as compared to existing approaches.