Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
Understanding and Using Context
Personal and Ubiquitous Computing
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Position-Annotated Photographs: A Geotemporal Web
IEEE Pervasive Computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
From context to content: leveraging context to infer media metadata
Proceedings of the 12th annual ACM international conference on Multimedia
Context data in geo-referenced digital photo collections
Proceedings of the 12th annual ACM international conference on Multimedia
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Multimodal metadata fusion using causal strength
Proceedings of the 13th annual ACM international conference on Multimedia
Scalable landmark recognition using EXTENT
Multimedia Tools and Applications
Methods for automatic and assisted image annotation
Multimedia Tools and Applications
Multimedia Tools and Applications
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This architecture paper presents EXTENT, a probabilistic framework that uses influence diagrams to fuse metadata of multiple modalities for photo annotation. EXTENT fuses contextual information (location, time, and camera parameters), photo content (perceptual features), and semantic ontology in a synergistic way. It uses causal strengths to encode causalities between variables, and between variables and semantic labels. Through a landmark-recognition case study, we show that EXTENT can provide high-quality annotation, substantially better than any traditional unimodal methods.