Abstract
In the past few years sharing photos, within social networks has become very popular .In order to make these huge collection easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order o speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context .In this paper, daily and continuous communication implies the exchange of several types of content, including free text, image, audio and video data. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments On an image database containing various landmarks. Tagging in online social networks is very popular these days as it facilitates search and retrieval of multimedia content .However, noisy and spam annotations often make it. Keywords - annotation process, audio and video data, trust modeling, tagging, spams.
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