Personalized annotation for mobile photos based on user’s social circle

Abstract

For mobile photos annotation, users are more interested in the context information behind the photos. The user’s social circle can provide valuable information for it. However, the accompanying textual information of social network is sparse and ambiguous in nature. In this paper, we propose a personalized annotation framework for mobile photos leveraging the user’s social circle. To address the unreliability problem of social network, we present an algorithm to generate reliable tags for social photos before assigning tags to the user’s unlabeled photos. In the tag generation stage, a multi-modality hierarchical clustering algorithm is performed to detect social events. Besides, we use “Album” instead of individual photo as the basic unit for clustering. Finally, we employ a weighted nearest neighbor model for label propagation. We evaluate our framework on a large-scale, real-world dataset from Renren, the largest Facebook-like social network in China. Our evaluation results show promising results of our proposed framework.