Crowd-sourced data and its applications for new algorithms in photographic imaging
This thesis comprises two main themes. The first of these is concerned primarily with the validity and utility of data acquired from web-based psychophysical experiments. In recent years web-based experiments, and the crowd-sourced data they can deliver, have been rising in popularity among the research community for several key reasons – primarily ease of administration and easy access to a large population of diverse participants. However, the level of control with which traditional experiments are performed, and the severe lack of control we have over web-based alternatives may lead us to believe that these benefits come at the cost of reliable data. Indeed, the results reported early in this thesis support this assumption. However, we proceed to show that it is entirely possible to crowd-source data that is comparable with lab-based results. The second theme of the thesis explores the possibilities presented by the use of crowd-sourced data, taking a popular colour naming experiment as an example. After using the crowd-sourced data to construct a model for computational colour naming, we consider the value of colour names as image descriptors, with particular relevance to illuminant estimation and object indexing. We discover that colour names represent a particularly useful quantisation of colour space, allowing us to construct compact image descriptors for object indexing. We show that these descriptors are somewhat tolerant to errors in illuminant estimation and that their perceptual relevance offers even further utility. We go on to develop a novel algorithm which delivers perceptually-relevant, illumination-invariant image descriptors based on colour names.
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