Abstract
Decorative openwork windows (DO-Ws) in Suzhou traditional private gardens play a vital role in Chinese traditional garden art. Due to the delicate and elegant patterns, as well as their rich cultural meaning, DO-Ws have quite high protection and utilization value. In this study, we firstly visited 15 extant traditional gardens in Suzhou and took almost 3000 photos to establish the DO-W datasets. Then, we present an effective visual recognition method named CSV-Net to classify different DO-Ws’ patterns in Suzhou traditional gardens. On the basis of the backbone module of the cross stage partial network optimized with the Soft-VLAD architecture, the proposed CSV-Net achieves a preferable representation ability for distinguishing different DO-Ws in practical scenes. The comparative experimental results show that the CSV-Net model achieves a good balance between its performance, robustness and complexity for identifying DO-Ws, also having further potential for sustainable application in traditional gardens. Moreover, the Canglang Pavilion and the Humble Administrator’s Garden were selected as the cases to analyze the relation between identifying DO-W types and their locations in intelligent approaches, which further reveals the design rules of the sustainable culture contained in Chinese traditional gardens. This work ultimately promotes the sustainable application of artificial intelligence technology in the field of garden design and inheritance of the garden art.
Highlights
Based on the research of Decorative openwork windows (DO-Ws) types, as well as the significant values highlighted above, three topics are proposed in this study: Firstly, can we make the identification and classification of DO-Ws more efficient and accurate with the help of modern technology? Secondly, can we identify the types of DO-W and their locations in certain gardens and analyze the relation between the design of their patterns and the garden’s surroundings in the meantime? Thirdly, which method of machine learning has the potential to be sustainably applied in DO-W designs and other fields of landscape design? From the issues and thoughts above, this paper summarizes the relevant research
In order to testify that our method could be well used for recognizing DO-W images, we establish some comparative experiments with other classical CNN methods, which have already achieved remarkable success in other fields, to illustrate the better performance of CSV-Net
The results show that the proposed method has a great influence on the learning of advanced features while performing network convergence, which further proves that our model is more stable than the other methods and is more suitable for the DO-W recognition task
Summary
As a significant style of Chinese traditional garden, private gardens in Suzhou contain characteristic local conditions and customs of regions south of the Yangtze River; as a result, the 21st meeting of the UNESCO World Heritage Committee approved recognizing the Humble Administrator’s Garden, the Lingering Garden, the Master of the Nets Garden and the Mountain Villa with Embracing Beauty as typical examples of “Suzhou traditional garden” included in the World Heritage List in 1997 They highly praised Suzhou gardens and commented that “art, nature, and ideas are integrated perfectly to create ensembles of great beauty and peaceful harmony” [1]
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