A comprehensive evaluation method for crowd-sourcing design work of product appearance integrating fractal dimension and hash algorithm

With the continuous development of social economy and the improvement of people’s living standards, consumers pay increasing attention to the appearance of products, color schemes and other emotional factors. As a result, the importance of the product appearance is highlighted. Crowdsourcing is a manifestation of collective wisdom, which is conducive to innovative design of product appearance, color and pattern. But due to the large number of participants, the evaluation of the work done by them becomes a difficult task. The fractal dimension serves as a reasonable statistical index for image complexity, while the image hash algorithm provides an efficient technique for measuring image similarity. Both can help analyze and judge the workload of images in product appearance design. In this paper, we propose a comprehensive evaluation model for crowdsourcing design work of product appearance, which integrates the image fractal dimension and hash algorithm with multi-dimensional assessment. Specifically, the fractal dimension is utilized to quantify the workload involved in image design. Meanwhile, an improved region—aware hash algorithm has been developed to better extract regional feature values from design images. Additionally, an expert evaluation mechanism has been incorporated into the model. Specific case studies and functional experimental tests have been conducted. The results indicate that the proposed method can effectively evaluate product appearance crowdsourcing design work, demonstrating its high practicality and accuracy.