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논문 기본 정보

자료유형
학술저널
저자정보
한유진 (연세대) 이현수 (연세대학교)
저널정보
대한건축학회 대한건축학회논문집 大韓建築學會論文集 第39卷 第2號(通卷 第412號)
발행연도
2023.2
수록면
121 - 128 (8page)

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초록· 키워드

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This study presents a deep learning approach to measuring a brand’s store image while generating positioning maps using social media data. Store design and architecture were highlighted as effective communicators of brand identity and positioning, but the spatial environment had been solely studied using traditional approaches such as surveys. This study adopted deep learning based CNN, which is an alternative methodology for evaluating a brand’s store image and created a positioning map in terms of interior design. Two axes were set to create a positioning map of style (X) and atmosphere (Y) that collected training data from Pinterest. Using the training dataset, this research employed Inception-V3 to retrain this model to evaluate the interior design. Based on the retrained model, the interior images of coffee shop brands were evaluated to determine each brand’s position and create a positioning map. Another positioning map was created based on a conventional method via a survey to demonstrate the validity of this approach. The results demonstrated that a brand’s store image can be trained and recognized using social data and deep learning technology. Additionally, brands’ relative positions and relationships can be assessed through a deep learning framework; therefore, a brand positioning map can be created. Various applications of these approaches in decision-making for brand store design, including the assessment of brand store positioning and redesigning stores were highlighted. Lastly, this study suggests wider uses for social big data and deep learning technology in branding and architectural design.

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Abstract
1. 서론
2. 이론 고찰
3. CNN을 활용한 점포 이미지 학습
4. 포지셔닝 맵 생성
5. 결론
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