Original Contribution: Stacked generalization
Neural Networks
WordNet: a lexical database for English
Communications of the ACM
Machine Learning - Special issue on inductive transfer
Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
The Journal of Machine Learning Research
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
Learning an image-word embedding for image auto-annotation on the nonlinear latent space
Proceedings of the 13th annual ACM international conference on Multimedia
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
Proceedings of the 13th annual ACM international conference on Multimedia
Image annotation by large-scale content-based image retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
SmartLabel: an object labeling tool using iterated harmonic energy minimization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Toward bridging the annotation-retrieval gap in image search by a generative modeling approach
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Inferring generic activities and events from image content and bags of geo-tags
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Event recognition: viewing the world with a third eye
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Quest for relevant tags using local interaction networks and visual content
Proceedings of the international conference on Multimedia information retrieval
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Tag suggestion on youtube by personalizing content-based auto-annotation
Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
Dynamic vocabularies for web-based concept detection by trend discovery
Proceedings of the 20th ACM international conference on Multimedia
Multifaceted conceptual image indexing on the world wide web
Information Processing and Management: an International Journal
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Automatic image annotation has been a hot-pursuit among multimedia researchers of late. Modest performance guarantees and limited adaptability often restrict its applicability to real-world settings. We propose tagging over time (T/T) to push the technology toward real-world applicability. Of particular interest are online systems that receive user-provided images and feedback over time, with user focus possibly changing and evolving. The T/T framework consists of a principled probabilistic approach to meta-learning, which acts as a go-between for a 'black-box' annotation system and the users. Inspired by inductive transfer, the approach attempts to harness available information, including the black-box model's performance, the image representations, and the WordNet ontology. Being computationally 'lightweight', this meta-learner efficiently re-trains over time, to improve and/or adapt to changes. The black-box annotation model is not required to be re-trained, allowing computationally intensive algorithms to be used. We experiment with standard image datasets and real-world data streams, using two existing annotation systems as black-boxes. Both batch and online annotation settings are experimented with. It is observed that the addition of this meta-learning layer produces much improved results that outperform best-known results. For the online setting, the T/T approach produces progressively better annotation with time, significantly outperforming the black-box as well as the static form of the meta-learner, on real-world data.