Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
International Journal of Computer Vision
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Building a comprehensive ontology to refine video concept detection
Proceedings of the international workshop on Workshop on multimedia information retrieval
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th international conference on Multimedia
Multi-cue fusion for semantic video indexing
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Fusing semantics, observability, reliability and diversity of concept detectors for video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event retrieval in video archives using rough set theory and partially supervised learning
Multimedia Tools and Applications
Measuring Concept Similarities in Multimedia Ontologies: Analysis and Evaluations
IEEE Transactions on Multimedia
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
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This paper examines video retrieval based on Query-By-Example QBE approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships e.g., generalization/specialization, sibling, part-of, and co-occurrence defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Dempster-Shafer theory is used to account the uncertainty in determining the relevance of a shot based on inaccurate annotation of this shot. Experimental results on TRECVID 2009 video data validate the effectiveness of the method.