Icon Similarity Algorithm Based on Skeleton Comparison

Abstract

Icon plays a crucial role in infographics, which additionally carries essential functions in the human-computer graphical user interface (GUI). However, too similar icon is easy to trigger confusion in the process of using. In this paper, we explored the use of the cognitive rules from global to local based on the theory of topological perception and built a computational discrimination tool from the human perception to describe similarity. Screening out icons that are too similar is the primary purpose of this research to avoid errors in use. We utilized the skeleton algorithm to extract the global features of icons. The optimal subsequence bijection and Hungarian algorithm were used to compare the global skeleton of the icon. Accordingly, the similarity between the icons was calculated. To verify the proposed algorithm, we conducted a subjective cognitive experiment. Participants were asked to rank the similarity of the experimental materials and compare the results with the calculation outcomes. Results demonstrate that the proposed calculation methodology based on skeleton comparison is close to subjective cognition, which can effectively describe the human perception of icon similarity.