Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Model structure and reliable inference
Perception as Bayesian inference
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
A Bayesian Video Modeling Framework for Shot Segmentation and Content Characterization
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Classifying Objects from Visual Information
Classifying Objects from Visual Information
On the choice of similarity measures for image retrieval by example
Proceedings of the tenth ACM international conference on Multimedia
Bayesian Learning for Image Retrieval Using Multiple Features
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Information Mining: Applications in Image Processing
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
Image Database Assisted Classification
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Image Retrieval from the World Wide Web: Issues, Techniques, and Systems
ACM Computing Surveys (CSUR)
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Spatio-temporal pattern mining in sports video
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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Current systems for content filtering, browsing, and retrieval rely on low-level image descriptors which are un intuitive for most users. In this paper, we propose an alternative framework that exploits the structured nature of most content sources to achieve semantic content characterization, and lead to much more meaningful user interaction. Computationally, this framework is based on the principles of Bayesian inference and can be implemented efficiently with Bayesian networks. As an illustration of its potential we apply it to the domain of movie databases.