4.6 Article

Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images

期刊

APPLIED SCIENCES-BASEL
卷 10, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/app10207299

关键词

interior design; reference image; deep learning; image recognition; design style; database

资金

  1. National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019R1A2C1007920]
  2. National Research Foundation of Korea [2019R1A2C1007920, 5199990314045] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

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