Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
The Journal of Machine Learning Research
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
SheepDog: group and tag recommendation for flickr photos by automatic search-based learning
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Knowledge discovery over community-sharing media: from signal to intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Semi-automatic flickr group suggestion
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Modeling social strength in social media community via kernel-based learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Children and geotagged images: quantitative analysis for security risk assessment
International Journal of Electronic Security and Digital Forensics
Recommending Flickr groups with social topic model
Information Retrieval
Interactive social group recommendation for Flickr photos
Neurocomputing
| Hi-index | 0.00 |
Popular photo-sharing sites have attracted millions of people and helped construct massive social networks in Cyberspace. Different from traditional social relationships, users actively interact in groups where common interests are shared on certain types of events or topics captured by photos and videos. Contributing images to an interest group would greatly promote interactions between users and expand their social networks. In this work, we intend to produce automatic recommendations of a user's images to suitable photo-sharing groups. To this end, we begin with analyzing user annotations and modeling the shared images in a group. Both visual content and annotation context are then integrated to understand the events or topics depicted in those images. Experiments on over 14000 user images demonstrate the feasibility of the proposed approach.