### Use Case  
In this section we present a use case to illustrate the proposed approach. We assume that a user uploads a photo of a fire juggling act taken at night with long exposure to Flickr (Figure 1A). We further assume that the user annotates the photo with a single tag: _juggling_. Based on this tag, a number of related tags can be suggested using tag co-occurrence, for example: _clown_, _fire_, _show_, _clubs_ and _juggler_. Figure 2 shows an ego-centered network depicting the relations between the tag juggling (in the center) and the suggested related tags surrounding it.  
Based on the ego-centered network one can assume that the tags _clown_, _fire_, _show_, _clubs_ and _juggler_ are good candidates to being presented to the user as suggested tags for the input image. However, in typical scenarios, the ego-centered network is not limited to five tags, but may  
Figure 1: Photos motivating our use case (the first one is the input image with one tag assigned by the user, the others have been previously uploaded by other users and their respective owners have assigned the tags shown below each image [10]).  
Figure 2: Ego-centered network of related tags around the tag _juggling_.  
contain 10-20 times as many, which makes it cumbersome for the user to traverse the list of suggested tags and select the ones she may be interested in. One possible solution is to select the one tag which co-occurs most often with the given tag, but this, too, is subject to errors. In the given example, if we assume that the highest-ranked tag in the suggested tags list is _balls_, we are obviously mistaken.  
Therefore we need an alternative ranking approach that allows for visual similarity to be factored into the tag recommendation process, promoting visually similar images (such as the one in Figure 1B) to appear closer to the top of the ranked tag suggestion list, in spite of the relatively low co-occurrence of the tags _juggling_ and _fire_. In the architecture proposed in this paper (described in detail in Section 3), we are interested in exploiting visual properties of photos, such as: limited range of hues (mostly yellow to red), large dark areas, and noticeable amount of noise (especially if taken with inexpensive cameras) due to the long exposure involved. This can be done with a principled selection of (pixel-based) feature extraction algorithms and dissimilarity metrics from the field of CBIR and the adoption of the well-known query-by-example (QBE) paradigm, whereby an example image (the one in Figure 1A, in this case) is provided as an example and visually similar images are retrieved from the database. The results of this implicit QBE step, where the example is the image that has just been uploaded, can then be used to strip tags that are assigned to images not visually similar to the initial one. We assume that this leads to a recommendation of better tags in terms of content description. This can result in our example in ranking _fire_ and _night_ higher, while possibly demoting the tag _balls_ from its top position. Consequently, the image in Figure 1B would be ranked higher than the images in Figures 1C and 1D, thereby improving the quality of the tag suggestion process and--perhaps more importantly--allowing the user to retrieve an image that could otherwise have gone undetected (due to the relatively low co-occurrence of the tags _juggling_ and _fire_).