The nature of statistical learning theory
The nature of statistical learning theory
Multidimensional access methods
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Semantic Queries with Pictures: The VIMSYS Model
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Mining images on semantics via statistical learning
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster-based data modeling for semantic video search
Proceedings of the 6th ACM international conference on Image and video retrieval
Segmentation and recognition of motion capture data stream by classification
Multimedia Tools and Applications
Ontology-enriched semantic space for video search
Proceedings of the 15th international conference on Multimedia
Semantic concept-based query expansion and re-ranking for multimedia retrieval
Proceedings of the 15th 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
Image retrieval using query by contextual example
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A framework for classifier adaptation and its applications in concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Metadata-driven interactive web video assembly
Multimedia Tools and Applications
Video PowerSearcher: a text-based indexing e-learning system
Proceedings of the 1st International Conference on Intelligent Semantic Web-Services and Applications
Using high-level semantic features in video retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Scene aligned pooling for complex video recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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Semantic understanding of multimedia content is critical in enabling effective access to all forms of digital media data. By making large media repositories searchable, semantic content descriptions greatly enhance the value of such data. Automatic semantic understanding is a very challenging problem and most media databases resort to describing content in terms of low-level features or using manually ascribed annotations. Recent techniques focus on detecting semantic concepts in video, such as indoor, outdoor, face, people, nature, etc. This approach works for a fixed lexicon for which annotated training examples exist. In this paper we consider the problem of using such semantic concept detection to map the video clips into semantic spaces. This is done by constructing a model vector that acts as a compact semantic representation of the underlying content. We then present experiments in the semantic spaces leveraging such information for enhanced semantic retrieval, classification, visualization, and data mining purposes. We evaluate these ideas using a large video corpus and demonstrate significant performance gains in retrieval effectiveness.