Sequential Gallery for Interactive Visual Design Optimization
This addresses the challenge for non-expert users in navigating complex design spaces, though it is incremental as it builds on existing Bayesian optimization and interface techniques.
The paper tackles the problem of efficiently tuning many design parameters in visual tasks by proposing a user-in-the-loop optimization method that reduces high-dimensional searches to easier two-dimensional subtasks, showing in experiments that it finds satisfactory solutions in fewer iterations and helps novices complete photo-enhancement tasks effectively.
Visual design tasks often involve tuning many design parameters. For example, color grading of a photograph involves many parameters, some of which non-expert users might be unfamiliar with. We propose a novel user-in-the-loop optimization method that allows users to efficiently find an appropriate parameter set by exploring such a high-dimensional design space through much easier two-dimensional search subtasks. This method, called sequential plane search, is based on Bayesian optimization to keep necessary queries to users as few as possible. To help users respond to plane-search queries, we also propose using a gallery-based interface that provides options in the two-dimensional subspace arranged in an adaptive grid view. We call this interactive framework Sequential Gallery since users sequentially select the best option from the options provided by the interface. Our experiment with synthetic functions shows that our sequential plane search can find satisfactory solutions in fewer iterations than baselines. We also conducted a preliminary user study, results of which suggest that novices can effectively complete search tasks with Sequential Gallery in a photo-enhancement scenario.