Query based visual analysis: spatio-temporal reasoning in computer vision
Image and Vision Computing
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A unifying review of linear Gaussian models
Neural Computation
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Pattern Recognition Letters - Special issue on image/video indexing and retrieval
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CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
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An Ontology for Video Event Representation
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IEEE Transactions on Image Processing
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Fuzzy spatial relation ontology for image interpretation
Fuzzy Sets and Systems
Video Object Segmentation Based on Feedback Schemes Guided by a Low-Level Scene Ontology
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CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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Expert Systems with Applications: An International Journal
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This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach. These models can integrate different sources of information both horizontally (multi-modal and temporal fusion) and vertically (bottom–up, top–down) by incorporating prior hierarchical knowledge expressed as an extensible ontology.Two implementations of this approach are presented. The first consists of a content-based image retrieval system that allows users to search image databases using an ontological query language. Queries are parsed using a probabilistic grammar and Bayesian networks to map high-level concepts onto low-level image descriptors, thereby bridging the ‘semantic gap’ between users and the retrieval system. The second application extends the notion of ontological languages to video event detection. It is shown how effective high-level state and event recognition mechanisms can be learned from a set of annotated training sequences by incorporating syntactic and semantic constraints represented by an ontology.