Behind each photographic act is a rationale that impacts the visual appearance
of the resulting photo. Better understanding of this rationale has great
potential to support image retrieval systems in serving user needs. However,
at present, surprisingly little is known about the connection between what
a picture shows (the literally depicted conceptual content) and why that picture
was taken (the photographer intent). In the thesis, we investigate photographer
intent in a large Flickr data set. First, an expert annotator carries
out a large number of iterative intent judgments to create a taxonomy of intent
classes. Next, analysis of the distribution of concepts and intent classes
reveals patterns of independence both at a global and user level. Finally,
we report the results of experiments showing that a deep neural network
classifier is capable of learning to differentiate between these intent classes,
and that these classes support the diversification of image search results.