Training products of experts by minimizing contrastive divergence
Neural Computation
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
A fast learning algorithm for deep belief nets
Neural Computation
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Evaluation of active learning strategies for video indexing
Image Communication
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Video corpus annotation using active learning
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Tagging tagged images: on the impact of existing annotations on image tagging
Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
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This paper explores the concept of image-wise tagging. It introduces a web-based user interface for image annotation, and a novel method for modeling dependencies of tags using Restricted Boltzmann Machines which is able to suggest probable tags for an image based on previously assigned tags. According to our user study, our tag suggestion methods improve both user experience and annotation speed. Our results demonstrate that large datasets with semantic labels (such as in TRECVID Semantic Indexing) can be annotated much more efficiently with the proposed approach than with current class-domain-wise methods, and produce higher quality data.