Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Ranking and classifying attractiveness of photos in folksonomies
Proceedings of the 18th international conference on World wide web
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
Semi-automatic flickr group suggestion
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Interactive social group recommendation for Flickr photos
Neurocomputing
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Photo-sharing services have attracted millions of people and helped construct massive social networks on the Web. A popular trend is that users share their image collections within social groups, which greatly promotes the interactions between users and expands their social networks. Existing systems have difficulties in generating satisfactory social group suggestions because the images are classified independently and their relationship in a collection is ignored. In this work, we intend to produce suggestions of suitable photo-sharing groups from a user's personal photo collection by mining images on the Web and leveraging the collection context. Both visual content and textual annotations are integrated to generate initial prediction of the events or topics depicted in the images. A user collection-based label propagation method is proposed to improve the group suggestion by modeling the relationship of images in the same collection as a sparse weighted graph. Experiments on real user images and comparisons with the state-of-the-art techniques demonstrate the effectiveness of the proposed approaches.