Discovering semantic and visual hints with machine learning of real design templates to support insight exploration in informatics

Ideation is the process by which ideas are populated by exploring visuals and semantics as essential stimuli when dealing with creative problem-solving. Knowledge of such activity often desires frequent changes in user goals and insights. A common technique that utilizes pre-designed contents with semantic-visual concepts for ideation raises uncertainty in informatics. This adaptive workflow supports the exploration of many text descriptions and image previews from software products and service design templates. With three steps: 1) Extract and summarize semantic-visual features from design contents. 2) Use clusters of adapted information for multi-label classification. 3) Construct a design exploration model with visualization and exploration. This study has provided a glanceable workflow of ideation. Hence, engineering of the design knowledge supports semantic and visual hints for emerging pathways of continuous ideation and design exploration.

Automated summary

Ideation is the process of generating ideas through exploring visual and semantic stimuli for creative problem-solving. This process often requires changes in user goals and insights. Using pre-designed content and semantic-visual concepts for ideation can introduce uncertainty. An adaptive workflow is proposed in this study that involves extracting and summarizing semantic-visual features, using clusters of adapted information for multi-label classification, and constructing a design exploration model with visualization and exploration.