A Cloud-Assisted Framework for Bag-of-Features Tagging in Social Networks

Abstract

Recently, Bag-of-Features Tagging is proven to be an alternative to discover user connections from user shared images in social networks. This approach used unsupervised clustering to classify the user shared images and then correlate similar user, which is computationally intensive for real-world applications. This paper introduces a cloud-assisted framework to improve the efficiency and scalability of Bag-of-Features Tagging. The framework distributes the computation of the unsupervised clustering, the profile learning process and also the similarity calculation. The experiment proves how a scalable cloud-assisted framework outperforms a stand-alone machine with different parameters on a real social network dataset, Skyrock.