Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality - Hung-Hsiang Wang; Ching-Yi Chen (Engineering Proceedings)
Brand typicality is crucial in shaping consumer perceptions of brands and poses challenges for novice designers to capture due to their limited tacit knowledge. Using Weka’s image classification, we developed a brand product classification model. A dataset with 600 images was obtained from Asus and MSI, the leading eSports brands, covering various products such as controllers, mouse devices, headsets, and PC gaming components. The random forest classifier achieved an accuracy of 81 to 85%, slightly higher in the PC gaming category. The design features from Asus ROG and MSI game series products were extracted to generate 36 test images. We used keywords as prompts in Midjurney and Stable Diffusion to generate 36 test images. The developed brand product classification model in this study correctly classified 30 images. However, in the OP category, two graphics card images and one casing image were misclassified. In the PC category, two mouse images and a laptop picture were misclassified. Discrepancies between AI-generated images and personal expertise were improved in terms of the efficiency of the model for new designers. The developed model deepens the understanding of brand characteristics, maintains brand coherence, and strengthens product design innovation and market competitiveness. The model effectively assesses brand characteristics in product appearances using AI, highlighting its role in improving early design processes and new product development strategies.