In: Proceedings of the 19th ACM International Conference on Multimedia, pp. Li, X., Gavves, E., Snoek, C.G., Worring, M., Smeulders, A.W.: Personalizing automated image annotation using cross-entropy. In: 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. Lei, C., Liu, D.: Image annotation via social diffusion analysis with common interests. In: Advances in Neural Information Processing Systems, pp. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: 2009 IEEE 12th International Conference on Computer Vision, pp.

Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. Gallagher, A.C., Neustaedter, C.G., Cao, L., Luo, J., Chen, T.: Image annotation using personal calendars as context. ACM (2010)Дatta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. In: Proceedings of the International Conference on Multimedia, pp. Multimedia 11(2), 208вЂ“219 (2009)Д¬heng, A.J., Lin, F.E., Kuo, Y.H., Hsu, W.H.: Gps, compass, or camera? Investigating effective mobile sensors for automatic search-based image annotation. ACM (2010)Д¬ao, L., Luo, J., Kautz, H., Huang, T.S.: Image annotation within the context of personal photo collections using hierarchical event and scene models. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. KeywordsД«ecker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. Our evaluation results show promising results of our proposed framework. We evaluate our framework on a large-scale, real-world dataset from Renren, the largest Facebook-like social network in China. Finally, we employ a weighted nearest neighbor model for label propagation. Besides, we use вЂњAlbumвЂќ instead of individual photo as the basic unit for clustering. In the tag generation stage, a multi-modality hierarchical clustering algorithm is performed to detect social events. To address the unreliability problem of social network, we present an algorithm to generate reliable tags for social photos before assigning tags to the userвЂ™s unlabeled photos. In this paper, we propose a personalized annotation framework for mobile photos leveraging the userвЂ™s social circle. However, the accompanying textual information of social network is sparse and ambiguous in nature. The userвЂ™s social circle can provide valuable information for it.

For mobile photos annotation, users are more interested in the context information behind the photos.

Personalized ephotos