Exploring Machine learning application in exhibition layout of museum

Abstract

Continual revisions and enhancements to the presentation in museum will allow visitors engagement to remain high interest and acquire visiting benefits when interaction within the display objects. The layout task of objects in exhibition gallery of museum is quite complex, high-cost, time-consuming, and laborious manual process. It is essential and necessary to establish a customized recommendation scheme of exhibition spatial layouts to provide museum crews the configuration frameworks of gallery to improve the efficient of exhibition layout. According to the interactive experience model in museums, we suggest three dimensions: the visitors' behavior, the role of objects, and the layout of space, will benefit to looking for affective and embodied procedures and physical principles of exhibition layout. On the other hand, the state-of-the-art machine learning of artificial intelligence has been widely applied in lots of professional fields (e.g. diagnosis, monitory, prediction, classification, interpretation, scheduling). According to the attributions of exhibition layout and the characteristic of machine learning methods, we suggest that machine learning is a great potential and powerful approach to build up a customized recommendation scheme of exhibition layouts based on the previous knowledge of layout, and it is worth to develop and implement in future research.