Product Color Design Concept that Considers Human Emotion Perception: Based on Deep Learning and Cluster Analysis

Abstract

Emotions play a significant role in how we perceive and interact with products. Thoughtfully designed emotionally appealing products can evoke strong user responses, making them more attractive. Color, as a crucial attribute of products, is a significant aspect to consider in the process of emotional product design. However, users’ emotional perception of product colors is highly intricate and challenging to define. To address this, this research proposes a product color design concept that considers human emotion perception based on deep learning and cluster analysis. First, for a given product, a color style is chosen for rerendering, which is an emotional color image. Different emotional color images have distinct RGB color representations. Second, clustering methods are employed to establish relationships between various emotional color images and different colors, selecting emotionally close style images. Subsequently, through transfer learning techniques, specific grid structures are used to retrain network weights, allowing for the fusion design of style and content images. This process ultimately achieves emotional color rendering design based on emotional color clustering and transfer learning. Multiple sets of emotional color design examples demonstrate that the method proposed in this study can accurately fulfill the emotional color design requirements of products, thereby, offering practical applicability. The satisfaction survey shows that the proposed method has certain guiding significance for clothing emotional color design.