Exploring Visual Cues for Design Analogy: A Deep Learning Approach

Abstract

Abstract The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework to augment designers’ visual analogical thinking by stimulating them by providing relevant visual cues from a variety of categories. Two steps are taken to reach this goal: developing a flexible computational framework to explore various visual cues, i.e., shapes or sketches, based on the relevant datasets and conducting human-based behavioral studies to validate such visual cue exploration tools. This article presents the results and insights obtained from the first step by addressing two research questions: How can the computational framework CAVAS be developed to provide designers in sketching with certain visual cues for stimulating their visual thinking process? How can a computation tool learn a latent space, which can capture the shape patterns of sketches? A visual cue exploration framework and a deep clustering model CAVAS-DL are proposed to learn a latent space of sketches that reveal shape patterns for multiple sketch categories and simultaneously cluster the sketches to preserve and provide category information as part of visual cues. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Performance evaluations of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The results have demonstrated the applicability of the CAVAS-DL model as the basis for the human-based validation studies in the next step.