Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Integrating Image Segmentation and Classification for Fuzzy Knowledge-Based Multimedia Indexing
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Reasoning within fuzzy description logics
Journal of Artificial Intelligence Research
Video retrieval using high level features: exploiting query matching and confidence-based weighting
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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Automated detection of semantic concepts in multimedia documents has been attracting intensive research efforts over the last years. These efforts can be generally classified in two categories of methodologies: the ones that attempt to solve the problem using discriminative methods (classifiers) and those that build knowledge-based models, as driven by the W3C consortium. This paper proposes a methodology that tries to combine both approaches for multimedia retrieval. Our main contribution is the adoption of a formal model for defining concepts using logic and the incorporation of the output of concept classifiers to the computation of annotation scores. Our method does not require the computationally intensive training of new classifiers for the concepts defined. Instead, it employs a knowledge-based mechanism to combine the output score of existing classifiers and can be used for either detecting new concepts or enhancing the accuracy of existing detectors. Optimization procedures are employed to adapt the concept definitions to the multimedia corpus in hand, further improving the attained accuracy. Experiments using the TRECVID2005 video collection demonstrate promising results.