Social image retrieval is increasingly important for managing and accessing
the rapidly growing social-tagged images. In this chapter, we address social
image retrieval from two directions which tackle the subjectiveness and the incompleteness
of social tagging, respectively. To make subjective social tagging objective,
we introduce a simple and effective neighbor voting algorithm to estimate the relevance
of a tag with respect to the visual content it is describing. To build a concept
index for untagged or incompletely tagged images, we study a new learning scenario
where concept detectors are trained with negative examples created by social
tagging, rather than by traditional expert labeling. Empirical studies on realistic
subsets of Flickr data demonstrate the potential of the proposed algorithms for
searching (un)tagged social images.
Keywords: Image retrieval, social tagging, machine tagging, tag relevance learning, concept
detection, negative examples, neighbor voting, concept index, expert labeling,
untagged