Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Texture Features for Browsing and Retrieval of Image Data
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Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Support vector machine active learning for image retrieval
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Understanding and Using Context
Personal and Ubiquitous Computing
Visually Searching the Web for Content
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Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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IEEE Pervasive Computing
Distinctive Image Features from Scale-Invariant Keypoints
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Context data in geo-referenced digital photo collections
Proceedings of the 12th annual ACM international conference on Multimedia
EXTENT: fusing context, content, and semantic ontology for photo annotation
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Journal of Artificial Intelligence Research
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Refining image annotation using contextual relations between words
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CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access
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We propose a probabilistic framework that uses influence diagrams to fuse metadata of multiple modalities for photo annotation. We fuse contextual information (location, time, and camera parameters), visual content (holistic and local perceptual features), and semantic ontology in a synergistic way. We use causal strengths to encode causalities between variables, and between variables and semantic labels. Through analytical and empirical studies, we demonstrate that our fusion approach can achieve high-quality photo annotation and good interpretability, substantially better than traditional methods.