Future Internet_**2010**, \(2\), 341-362; doi:10.3390/fi2030341  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Can Global Visual Features Improve Tag Recommendation**  
**for Image Annotation? **  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 3**  
Footnote 1: [[http://www.mdpi.com/journals/](http://www.mdpi.com/journals/)]([http://www.mdpi.com/journals/](http://www.mdpi.com/journals/))  
technologies, accompanied by a significant reduction in hardware costs, our picture-taking habits have changed dramatically. We live in a world where more people have cameras than ever before; the average number of pictures taken per person is at least an order of magnitude higher than it used to be during the pre-digital era, and many of these pictures are uploaded to social sharing sites at an astounding rate to be viewed by an audience of millions. For example, Flickr (www.flickr.com), a well known web platform for storing, organizing and sharing photos, has more than 26 million members [1] and grows by more than 6000 photos uploaded each minute [2]. Despite all these advances in creating and storing images, the tasks of finding images of interest and retrieving them remain as challenging as ever. One of the main difficulties in image retrieval is the fact that most successful search engines are text-based and therefore rely on the presence of text (e.g., keywords) associated with the images to be able to properly retrieve them. In the case of social sharing sites, such keywords usually appear as _tags_ associated with the images. In a perfect world, all images would have a reasonable number of user-generated tags, which would then enable other users to find and retrieve them. Unfortunately, in reality, only a fraction of the uploaded pictures are tagged with useful tags by their users, leaving an enormous number of (potentially good and interesting) pictures buried in a place that keyword-based search engines cannot reach. Since the early 1990s, the desire to be able to locate and retrieve images regardless of textual metadata has formed the motivation for a field of research known as _content-based image retrieval_ (CBIR), which emerged from the crossroads of the fields of computer vision, databases and information retrieval. After a promising start, CBIR researchers realized that their efforts were being significantly hampered by what became known as the'semantic gap', which refers to the inability of a machine to fully understand and interpret images based on automatically extracted low-level visual features, such as predominant texture, color layout or color distributions [3]. The obstacles imposed by such a gap have limited the success of pure CBIR solutions to narrow domains. Much of current research in CBIR--or the broader field of visual information retrieval (VIR)--is aimed at reducing the semantic gap and incorporating textual information in order to improve the overall quality of the retrieval results [4]. For several years, one of the main obstacles faced by researchers trying to combine visual data and textual metadata was the long-held assumption that manual image annotation is too expensive, subjective, biased, and ultimately, not feasible. This assumption has recently been challenged in many ways, e.g., the availability of Semantic Web-related ontologies [5], the popularity of image labeling games [6], and the willingness of users to annotate, tag, rate, and comment on pictures, enabled by social media sharing sites [7]. The latter aspect, namely the increasing availability of user-generated tags, combined with the successful track record of CBIR within narrow domains, has motivated this work. However, manually tagging images is an extremely time consuming task. Automatic tagging systems may be able to address this problem by tagging images autonomously as they are uploaded. Yet, one obvious problem is that images may be incorrectly tagged or that important concepts may be skipped entirely.