## 2 Related Work  
Research efforts towards semantically-capable visual information retrieval systems have grown exponentially over the past five years. Some of these efforts are tied to Semantic Web standards, languages and ontologies [11], while others employ keywords in a loose way (not associated with any ontology or folksonomy) [12]. Still others rely on tags (e.g., [6]) and are therefore more closely related to the work proposed in this paper. Tags assigned by users are often ambiguous, available in several languages or declinations and sometimes not even related to the image content at all [13]. Despite these shortcomings, social taggingoften leads to surprisingly good annotations extracted from a huge amount of annotated content due to the \"wisdom of the crowds\" effect--the collective knowledge of the user community [8]. Tags may be applied, searched and stored in a very easy fashion; are not restricted to a fixed vocabulary, but instead may be personalized and are just as suitable for small as for large collections [7]. Research on social tagging systems is rather young and therefore continuously expanding into new directions. This section provides an overview of the prominent papers which are relevant for our work in this field. In [14], [PERSON] analyzes the concept of _folksonomies_, which are the result of social annotations, a network of users, resources and tags, under the assumption that they are social ontologies. [PERSON] presents and discusses an approach for co-assignment analysis in folksonomies, which serves as a basis for part of our work. Network properties in tag co-assignment networks are discussed in [15, 16]. In [17], association mining and tag recommendation within social tagging systems is discussed. [PERSON] _et al._[18] define a relevance function for retrieval in folksonomies based on PageRank. Tag recommendation for images has also been discussed by [PERSON] _et al._ in [19]. After conducting comprehensive experiments, [PERSON] _et al._ found that for 40% of the images in their test data set from Flickr, correct tags are ranked rather highly. The approach of [PERSON] _et al._[20] is related to our work to the extent that they also postulate that a combination of content-based image features and tags enhances image management. However, while we focus on supporting the annotation process to improve and extend the quality of annotations, in [20] the focus is put on reducing the negative effects of mistaken tags (typos and false tags), synonymy and homonymy for retrieval in image databases. All in all, a lot of work has been done on auto-tagging lately. Examples are for instance [PERSON] _et al._[34], who propose an auto-tagging approach for images based on visual information retrieval. The nearest neighbors in terms of color and texture are determined and labels are transferred from the result set to the query image. [PERSON] _et al._[35] also present a method for auto-tagging employing the vast amount of already tagged photos in the internet. Like in [34] labels from visually similar images are transferred to a query image. In [36] [PERSON] _et al._ propose a tag relevance scheme based on visual similarity of tagged images. All these auto-tagging approaches have one problem in common: the semantic gap [4]. With our approach of tag recommendation, where at least one start tag is given, the domain of possible photos is reduced by filtering photos by the given tag(s). Therefore we operate in a small domain, where success to bridge the semantic gap is more likely than in broad and general use cases. [PERSON] and [PERSON] [21] examine the problem of supporting users in the tagging process on a general level. After re-considering the fundamentals of tagging, they explore the prerequisites for implementing tagging in a diverse range of systems outside the traditional strongholds of photography, social networking and bookmarking.