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Sequential Line Search for Efficient Visual Design Optimization by Crowds
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DOI：10.1145/3072959.3073598.png)
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Abstract
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Parameter tweaking is a common task in various design scenarios. For example, in color enhancement of photographs, designers tweak multiple parameters such as brightness and contrast to obtain the best visual impression. Adjusting one parameter is easy; however, if there are multiple correlated parameters, the task becomes much more complex, requiring many trials and a large cognitive load. To address this problem, we present a novel extension of Bayesian optimization techniques, where the system decomposes the entire parameter tweaking task into a sequence of one-dimensional line search queries that are easy for human to perform by manipulating a single slider. In addition, we present a novel concept called crowd-powered visual design optimizer, which queries crowd workers, and provide a working implementation of this concept. Our single-slider manipulation microtask design for crowdsourcing accelerates the convergence of the optimization relative to existing comparison-based microtask designs. We applied our framework to two different design domains: photo color enhancement and material BRDF design, and thereby showed its applicability to various design domains.
Keywords:
Bayesian optimization
crowdsourcing
human computation
computational design
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