Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tagging over time: real-world image annotation by lightweight meta-learning
Proceedings of the 15th international conference on Multimedia
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Image annotation using personal calendars as context
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Foundations and Trends in Information Retrieval
TubeTagger - YouTube-based Concept Detection
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Quest for relevant tags using local interaction networks and visual content
Proceedings of the international conference on Multimedia information retrieval
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
The task-dependent effect of tags and ratings on social media access
ACM Transactions on Information Systems (TOIS)
Personalizing automated image annotation using cross-entropy
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Large scale evaluations of multimedia information retrieval: the TRECVid experience
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Learning Visual Contexts for Image Annotation From Flickr Groups
IEEE Transactions on Multimedia
Fusing concept detection and geo context for visual search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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We address the challenge of tag recommendation for web video clips on portals such as YouTube. In a quantitative study on 23,000 YouTube videos, we first evaluate different tag suggestion strategies employing user profiling (using tags from the user's upload history) as well as social signals (the channels a user subscribed to) and content analysis. Our results confirm earlier findings that --~at least when employing users' original tags as ground truth~-- a history-based approach outperforms other techniques. Second, we suggest a novel approach that integrates the strengths of history-based tag suggestion with a content matching crowd-sourced from a large repository of user generated videos. Our approach performs a visual similarity matching and merges neighbors found in a large-scale reference dataset of user-tagged content with others from the user's personal history. This way, signals gained by crowd-sourcing can help to disambiguate tag suggestions, for example in cases of heterogeneous user interest profiles or non-existing user history. Our quantitative experiments indicate that such a personalized tag transfer gives strong improvements over a standard content matching, and moderate ones over a content-free history-based ranking.