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

Jinsung Kim,Jin-Kook Lee

doi:10.3390/app10207299

Abstract

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.

Highlights

In the architectural and interior design fields, referring to previous design works to solve new problems is a common approach [1,2]
The research steps can be summarized as follows: (1) Propose a conceptual model of interior design style information based on a style recognition model; (2) prepare an interior design style reference image dataset based on survey data for interior design styles; (3) train and evaluate the interior design style recognition model with a Convolutional neural networks (CNNs); and (4) apply the quantitative style information and databases with interior design style recognition (Figure 1)
This paper focuses on the deep learning-based approach to treating a qualitative information in terms of efficient management and utilization of interior design references

Summary

Introduction

In the architectural and interior design fields, referring to previous design works to solve new problems is a common approach [1,2]. Reference images are useful visual materials for communication during the design process because the general layperson (e.g., the client) prefers images that represent the design results [3]. Reference data must be appropriately interpreted, structured, classified, and organized to be reused efficiently [4]. Reference-sharing platforms generally provide reference images with various kinds of information about, for example, the designer, site, cost, and materials. Several types of information, such as the cost, designer, site location, and area, are static and do not need interpretation. Other types of information, such as the design style, are qualitative and change according to the different criteria or individuals

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