## 2 Related Work
In [25], [PERSON] and [PERSON] present a method for utilizing both an image's content and the context surrounding it to extract semantics. The premise is that interpreting high level semantic data is easier when surrounded by sufficient contextual information. The authors subsequently propose a system to fuse content information with two types of contextual information, namely optical and ontological, and demonstrate its effectiveness in classification and annotation tasks. The research of [PERSON] and [PERSON] is relevant to this work as an example of a system that attempts to classify and automatically tag photos by fusing metadata sources. Similar to the evaluation presented here, they used photos obtained from _Flickr_, citing that these are more representative than more homogenous professional datasets, such as the Corel image set [26]. They do, however, note that many of the photos available on _Flickr_ contain unreliable or incorrect tags and subsequently filtered photos and tags to obtain their test dataset. In their work, [PERSON] and [PERSON] focus on utilizing the optical content layer, using _aperture_, _exposure time_, _ISO_ and _focal length_ as parameters and defining the _LogLight_ metric, which can be used to determine the proximity of various combinations of camera settings and is designed to reflect the amount of ambient light available when the photo was taken. Using unsupervised clustering on a dataset consisting of the optical context of 30,000 photos, they produced eight representative clusters of settings that can be considered typical. Next they examined a smaller subset of 3,500 tagged photos, determining the most common tags for each cluster. The results can be considered surprising: the clusters contained mutually exclusive dominant tags, indicating that the clustering can expose semantic information. For example, the long exposure cluster included tags such as _night_, _fireworks_ and _moon_. [PERSON] and [PERSON] support their claim that optical context information may be used to derive higher level contextual information by building a classifier that places photos into one of three location classes (\"outdoor day\", \"outdoor night\" and \"indoor\") based on the _LogLight_ metric. Interestingly the classifier achieved an accuracy of 87.5% when using optical context alone. The authors then further extended the classifier with thumbnail image-based features such as average color, color histogram, edge histogram and Gabor texture features, yielding a 2% improvement in accuracy. They note that the high degree of accuracy obtained from the simplistic and compact optical context (typically a few bytes and insignificant compared to the image itself) is worth exploiting particularly when compared to the computationally expensive image features. The authors then turned their attention to the broader task of automatically tagging photos. They considered two datasets: one crawled and filtered from _Flickr_ and the other consisting of manually tagged photos. By applying a Bayes network model to the task, they were able to estimate the probability of a tag being applied to a photo in the testing set based on its frequency in the training set and similarity in image features. To demonstrate the applications of contextual information, the authors also integrated optical clustering based on the _LowLight_ metric to improve the accuracy of their model. The five most likely tags for each photo were then taken as suggestions and compared to the actual tags in terms of precision and recall. Results were mixed, achieving a precision of 0.22 and recall of 0.35 for the manually tagged dataset and a precision of 0.14 and recall of 0.27 for the _Flickr_ dataset. As a final step, and perhaps of most relevance to this work, they explored the possibility of improving the tagging system by integrating ontologies, or more specifically, by harnessing related tags. This makes sense as tags are rarely applied to photos in an independent fashion. For example, _fireworks_ and _night_ are likely to occur together.