Abstract
Online social image websites such as Flickr and Zooomr allow users to manually annotate their uploaded images with freely-chosen tags, which are then used as indexing keywords to facilitate image search and other applications. However, although the tags are provided by the users, they are generally imprecise and incomplete, and many of them are intrinsically unrelated to the visual content. Besides, the tags associated with social images are generally uniform without any importance or relevance information with respect to the content. The imprecise, incomplete and uniform characteristics of the tags have significantly limited tagbased applications, such as social image search and browsing. In this chapter, we discuss the tag processing techniques for improving the quality of the manually input tags for the social images on the Internet, which includes tag filtering, completing and ranking. We will also show various applications benefiting from the processed tags.
Keywords: social images, tag filtering, tag ranking, tag refinement, tag completion, tag enrichment, visual similarity, semantic similarity, ontology, graph, optimization
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