In computer graphics and user interface design, selection problems are
those that require the user to select a collection consisting of a
small number of items from a much larger library. This dissertation
explores selection problems in two diverse domains: large personal
multimedia collections, containing items such as personal photographs
or songs, and camera positions for 3D objects, where each item is a
different viewpoint observing an object. Multimedia collections have
by discrete items with strong associated metadata, while camera
positions form a continuous space but are weak in metadata. In either
domain, the items to be selected have rich interconnections and
dependencies, making it difficult to successfully apply simple
techniques (such as ranking) to aid the user. Accordingly, we develop
separate approaches for the two domains.
For personal multimedia collections, we leverage the semantic metadata associated with each item (such as song title, artist name, etc.) and provide the user with a simple query language to describe their desired collection. Our system automatically suggests a collection of items that conform to the user’s query. Since any query language has limited expressive power, and since users often create collections via exploration, we provide various refinement techniques that allow the user to expand, refine and explore their collection directly through examples.
For camera positioning, we do not have the advantage of having semantic metadata for each item, unlike in media collections. We instead create a proxy viewpoint goodness function which can be used to guide the solution of various selection problems involving camera viewpoints. This function is constructed from several different attributes of the viewpoint, such as how much surface area is visible, or how "curvy" the silhouette is. Since there are many possible viewpoint goodness functions, we conducted a large user study of viewpoint preference and use the results to evaluate thousands of different functions and find the best ones. While we suggest several goodness functions to the practitioner, our user study data and methodology can be used to evaluate any proposed goodness function; we hope it will be a useful tool for other researchers.