WordNet: a lexical database for English
Communications of the ACM
Convex Optimization
Cross-Generalization: Learning Novel Classes from a Single Example by Feature Replacement
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical classification for automatic image annotation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Factor graph framework for semantic video indexing
IEEE Transactions on Circuits and Systems for Video Technology
Context information exchange and sharing in a peer-to-peer community: a video annotation scenario
Proceedings of the 27th ACM international conference on Design of communication
A social approach to authoring media annotations
Proceedings of the 10th ACM symposium on Document engineering
Probabilistic image tagging with tags expanded by text-based search
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Tagging image by exploring weighted correlation between visual features and tags
WAIM'11 Proceedings of the 12th international conference on Web-age information management
In-video product annotation with web information mining
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Improving image tags by exploiting web search results
Multimedia Tools and Applications
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Classical machine learning methods, such as Support Vector Machines, by taking each concept detection as an independent classification problem, can not achieve a sound performance for image and video annotation due to the overfitting problems. Thus, some prior knowledge is required to assist the learning of independent concept detectors, e.g. some concepts look much more alike to each other. In this paper, we assume that visually similar concepts should share resembled detectors. Based on the assumption, Collaborative Learning is proposed, to incorporate cross-concept collaborations into the joint learning of similar detectors over related concepts. Besides the collaborations, different concepts should also perform discriminations for classifying each other. To benefit from different trade-offs between collaboration and discrimination, we propose Multi-Granularity Boosting strategy, where each granularity corresponds to a specific balance between collaboration and discrimination for Collaborative Learning. The ultimate concept detector is an additive model that combines classifiers under different collaboration granularities together. Evaluations on both image and video annotation benchmark demonstrate that our method achieves a superior performance over independent annotation.