| id |
caadria2020_342 |
| authors |
Han, Yoojin and Lee, Hyunsoo |
| year |
2020 |
| title |
A Deep Learning Approach for Brand Store Image and Positioning - Auto-generation of Brand Positioning Maps Using Image Classification |
| doi |
https://doi.org/10.52842/conf.caadria.2020.2.689
|
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 689-696 |
| summary |
This paper presents a deep learning approach to measuring brand store image and generating positioning maps. The rise of signature brand stores can be explained in terms of brand identity. Store design and architecture have been highlighted as effective communicators of brand identity and position but, in terms of spatial environment, have been studied solely using qualitative approaches. This study adopted a deep learning-based image classification model as an alternative methodology for measuring brand image and positioning, which are conventionally considered highly subjective. The results demonstrate that a consistent, coherent, and strong brand identity can be trained and recognized using deep learning technology. A brand positioning map can also be created based on predicted scores derived by deep learning. This paper also suggests wider uses for this approach to branding and architectural design. |
| keywords |
Deep Learning; Image Classification; Brand Identity; Brand Positioning Map; Brand Store Design |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (11,910,714 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:50 |
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