Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality
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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/89/1/8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850156523159814144 |
|---|---|
| author | Hung-Hsiang Wang Ching-Yi Chen |
| author_facet | Hung-Hsiang Wang Ching-Yi Chen |
| author_sort | Hung-Hsiang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2adfaefa08144fc087e992ded6b54fc2 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-2adfaefa08144fc087e992ded6b54fc22025-08-20T02:24:30ZengMDPI AGEngineering Proceedings2673-45912025-02-01891810.3390/engproc2025089008Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand TypicalityHung-Hsiang Wang0Ching-Yi Chen1Department of Industrial Design, National Taipei University of Technology, Taipei 10617, TaiwanDepartment of Industrial Design, National Taipei University of Technology, Taipei 10617, TaiwanBrand 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.https://www.mdpi.com/2673-4591/89/1/8eSports productbrand typicalityAI-generated imagesimage classificationWeka |
| spellingShingle | Hung-Hsiang Wang Ching-Yi Chen Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality Engineering Proceedings eSports product brand typicality AI-generated images image classification Weka |
| title | Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality |
| title_full | Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality |
| title_fullStr | Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality |
| title_full_unstemmed | Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality |
| title_short | Developing Weka-Based Image Classification Learning Model: Enhancing Novice Designers’ Recognition of Brand Typicality |
| title_sort | developing weka based image classification learning model enhancing novice designers recognition of brand typicality |
| topic | eSports product brand typicality AI-generated images image classification Weka |
| url | https://www.mdpi.com/2673-4591/89/1/8 |
| work_keys_str_mv | AT hunghsiangwang developingwekabasedimageclassificationlearningmodelenhancingnovicedesignersrecognitionofbrandtypicality AT chingyichen developingwekabasedimageclassificationlearningmodelenhancingnovicedesignersrecognitionofbrandtypicality |